Interview guides · DCM

DCM Software Engineer Interview Guide

DCM Software Engineer interview guide: online assessment, group discussion, personal interview, reported questions with answers and a 7-day plan.

The 60-second brief

DCM stands for Data Communications Management. Candidates describe it as a business that turns customer data into finished communications, either print-ready or digital-ready, using variable data printing (VDP), where one design template is merged with each record's own text, barcodes and images. The clients named in candidate reports include financial institutions and retailers. A Software Engineer, often titled Prepress Programmer, writes the scripts and logic that ingest raw client files, map the fields, drive the layout and keep the production pipeline moving, and also troubleshoots script failures and data mismatches and refactors older workflows.

This guide is for candidates applying to DCM as a Software Engineer or Prepress Programmer. It covers the three reported rounds, the reported technical and behavioral questions with the answer structures that suit them, the scripting and data-format skills the role lists, and a seven-day plan that moves from aptitude practice to the group discussion and the personal interview.

Candidates report three rounds over about 3-5 weeks: an online assessment, a collaborative evaluation run as a group discussion, and a personal interview that mixes technical and HR conversation.

Focus areas

Technical aptitude testing
Conceptual knowledge
Problem solving

Overview

The work candidates describe is pipeline work. Customer data arrives as files, a script validates and maps it, and a composition step produces the finished output for each record. The role lists Python, JavaScript or PowerShell as the scripting languages, strong SQL, and working knowledge of XML, JSON, CSV and PDF; the aptitude area also mentions C#, and postscript files appear among the formats. Familiarity with prepress tools such as HP SmartStream, Quadient Inspire or Kodak Prinergy is listed as a bonus rather than a requirement, so the safest investment is fluency in one scripting language and in the data formats themselves.

Because one bad record can stop a whole production queue, reported scenarios keep returning to defensive handling: unexpected nulls, malformed rows, a folder that receives a CSV that fails validation, a script that runs out of memory on a large PDF. Prepare answers that name what you check on input, what you do with a rejected record, what you log, and how a rerun stays safe. The same habits answer the error-logging, regex, secure file transfer and query-tuning questions in the reported list.

The loop is unusual in mixing three formats. The first stage tests aptitude and basic coding, so practise timed logic, pattern and word problems next to code. The second is a group discussion, which no amount of solo coding rehearses; it needs practice with other people. The third is a personal interview with engineering leaders and HR about your past projects, technical scenarios and career direction, so have two or three projects you can explain end to end, with the manual steps you removed and the failures you guarded against.

Candidates also suggest learning the basics of the print side even if your background is general software: what VDP is, what PDF normalisation means, and what layout imposition is. You do not need to be a prepress specialist, but a clear explanation of how a template and a data record combine into one page will help in both the technical conversation and the project discussion.

The DCM interview process

Candidates describe three stages in sequence, with about 3-5 weeks reported from start to finish. Treat the descriptions below as what candidates report rather than a published process.

Online Assessment

Candidates report this first stage as a screening of general aptitude and core technical knowledge. Candidate tips mention logical reasoning, pattern recognition and mathematical word problems, and they also report a technical test of coding logic and basic programming concepts under a time limit. Example scenarios from candidates include finding duplicate records in a dataset with millions of entries while watching both time and memory, and explaining how to debug a script that crashes with an out-of-memory error on a large PDF. Approach it as two skills: speed on aptitude items, and short correct code in your strongest language (Python, JavaScript or C# are the languages candidates mention). Before submitting, test the empty input, nulls and malformed values, since candidates are told that an unhandled null is the typical failure.

What to demonstrate

Logical reasoning and mathematical aptitude
Core data structures and step-by-step algorithmic thinking
Working code in a language of your choice, including edge cases such as nulls and bad formats
Reasoning about memory and file I/O when data is too large to hold at once

How to prepare

Do timed sets of logic, pattern and word problems, and track which type costs you the most time
Write the duplicate-records function two ways: an in-memory hash set keyed on the record's identifying fields, then a streaming or partitioned version for when the set no longer fits in memory
Rehearse the out-of-memory debugging sequence: confirm where memory grows, switch from loading the whole file to processing it in chunks, release references, and measure peak usage again
Review the conceptual foundations questions (synchronous versus asynchronous work, hash table lookup cost) so you can answer them in a few precise sentences

Collaborative Evaluation

Candidates describe this stage as a group discussion in which you work with other candidates on a problem. Reported scenarios include prioritising a backlog of feature requests for a client portal on a tight timeline, and deciding how a team would respond to a sudden system outage that affects physical print production at a peak business time. Candidate tips point to active listening, clear and jargon-free communication, working through disagreement to a shared conclusion, and acting as a facilitator who summarises progress and brings quieter people into the conversation. Approach it as a structured conversation, not a solo performance: agree the goal first, name the criteria you will use, and close by stating what the group decided.

What to demonstrate

Communication and structured presentation of ideas
Active listening and building on other people's points
Resolving differing opinions to a workable consensus
Breaking an ambiguous problem into manageable parts as a team

How to prepare

Rehearse both reported scenarios with two or three friends and record the session, then check how much you talked and whether you invited others in
For the backlog scenario, practise stating criteria out loud (client impact, deadline, effort, dependencies) before ranking anything
For the outage scenario, use a sequence you can say in order: contain the damage, tell affected people, find the cause, restore, then prevent a repeat
Practise a one-sentence summary you can offer mid-discussion and again at the end

Personal Interview

Candidates report a personal interview that combines technical and HR evaluation, with engineering leaders and HR representatives. Conversation covers your past projects, deeper technical scenarios and how your career direction fits the role. Candidate tips say to highlight automation work: projects where you removed manual steps, shortened processing, or automated file transfers, together with the testing, error logging and data validation behind them. Approach it with project walk-throughs you can take to any depth: the problem, your own decisions, the failure modes you guarded against and what you would change. Have the behavioral stories ready as well, including the production bug under a deadline, code-review feedback, cross-functional delivery and prioritisation.

What to demonstrate

Depth in the projects on your resume and your own part in them
Technical judgement on scenarios, including how you handle bad data and failures
Communication of technical ideas to engineering and non-engineering listeners
Fit between your career direction and the role's automation focus

How to prepare

Write a one-page outline for two projects: goal, your decisions, data formats and tools involved, failure handling, measurable result
Practise scenario answers using the workflow cases candidates describe, such as a monitored folder that receives CSV files to validate before a composition step, or a feed with unexpected nulls
Prepare clear spoken answers for SQL tuning, relational versus non-relational storage, parsing unstructured files, and secure file transfer
Rehearse the four behavioral stories in first person, each with the outcome and one thing you would change

Where candidates lose points

Treating the online assessment as a coding test only and skipping aptitude practice

Candidates mention logical reasoning, pattern recognition and mathematical word problems alongside the technical test. Do timed sets of those next to coding practice, and note which type slows you down.

Answering the duplicate-records question with code that loads every record into memory or compares each pair

Start with a hash set on the identifying fields, state its time and memory cost, then explain how you would stream, sort or partition the data when it no longer fits. Name the memory limit out loud.

Describing scripts for the happy path only, with no plan for nulls, malformed rows or a bad input file

For every parsing or automation answer, say what you check on input, where a rejected record goes, what you log with its line number, and whether the run continues or stops. Test nulls and empty input before you submit code.

Dominating the group discussion, or staying quiet and waiting for a turn

Open by agreeing the goal and criteria, restate someone else's point before adding yours, invite a quiet member in, and finish with a short summary of what the group decided.

Telling behavioral stories in the plural ('we fixed it') with no personal decision, outcome or lesson

Use first person for your own actions, state what you decided and why, give the result you can measure, and name one thing you would change. Practise all four reported stories out loud before the personal interview.

Questions & practice

Explain the difference between synchronous and asynchronous programming, and when you would use each.

Medium · Technical & Conceptual Foundations

Approach

Define the two models: a synchronous call blocks the caller until the operation returns, while an asynchronous call starts the operation and lets the caller continue, with the result delivered later through a callback, promise/future or await.
Separate async from parallel: an event loop (Node.js, Python asyncio) interleaves many waiting I/O operations on one thread. That helps I/O-bound work such as many SFTP downloads, HTTP calls or DB queries. It does not speed up CPU-bound work such as rendering PDFs.
Pick the tool by workload: async or a thread pool for high-concurrency I/O, and processes (multiprocessing, ProcessPoolExecutor) for CPU-heavy Python, because the GIL in standard CPython stops threads from running Python bytecode in parallel. Plain synchronous code is the right call for short, strictly sequential scripts, where it is easier to read and to debug.
Name the classic async bugs. A blocking call inside a coroutine (time.sleep, requests.get) stalls the whole event loop, so use the async library or asyncio.to_thread. Exceptions get lost when tasks are never awaited. Unbounded fan-out overloads the remote server, so cap it with a semaphore.

Follow-up

What happens if a coroutine calls a blocking library function, and how would you spot it?
How would you fetch 500 client files concurrently without overwhelming the source server?
Would you use threads, processes or asyncio to convert thousands of PDFs, and why?

Describe how a hash table works and its time complexity for search operations.

Medium · Technical & Conceptual Foundations

Approach

Mechanics: a hash function maps each key to a bucket index (hash modulo capacity). Collisions are resolved either by separate chaining (Java HashMap keeps a list per bucket) or by open addressing with probing (CPython dict). A lookup hashes the key, goes to the bucket, then compares keys for equality.
Complexity: search, insert and delete are O(1) on average with a good hash and a bounded load factor, and O(n) in the worst case when keys pile into one bucket. Java 8+ converts a bucket into a balanced tree once it holds more than 8 entries (with table capacity of at least 64), which caps that bucket at O(log n).
Resizing: when the load factor passes a threshold (0.75 by default in Java HashMap, about two-thirds full in CPython dict), the table grows and every entry is rehashed. That single insert costs O(n), but inserts stay amortized O(1).
Key contract and limits: equal keys must produce equal hashes, and a key that is mutated after insertion can no longer be found, which is why Python refuses lists as keys. Hash tables give no sorted order or range queries, so use a balanced BST or a sorted index when you need those.
Apply it: detecting duplicate records is one pass that puts a tuple of the identifying fields into a set, giving O(n) time and O(distinct keys) memory, compared with O(n^2) for pairwise comparison or O(n log n) for sort-and-scan.

Follow-up

How would you find duplicates when the distinct keys do not fit in memory?
Why can an attacker degrade a hash table to O(n) per lookup, and how do runtimes defend against it?
What happens if you change a field of an object after using it as a dictionary key?

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
Where does the fingerprint get computed relative to request decompression and the body-size limit?

Walk me through how you would optimize a slow-running database query.

Medium · Technical & Conceptual Foundations

Approach

Measure before changing anything: capture the exact query, its parameters and its runtime, then read the plan with EXPLAIN (ANALYZE, BUFFERS) in PostgreSQL, EXPLAIN ANALYZE in MySQL 8.0.18+, or the actual execution plan in SQL Server. EXPLAIN ANALYZE really executes the statement, so run writes inside a transaction and roll it back.
Read the plan for the expensive node: sequential scans on large tables, estimated row counts far off the actual ones (stale statistics, so run ANALYZE), nested loops over large inputs, and sorts or hashes spilling to disk.
Fix access paths: index the columns used in WHERE, JOIN and ORDER BY. In a composite index, put equality columns before range columns, and consider a covering index (INCLUDE) so the query never touches the table. Indexes cost write speed and storage, so add only what the plan justifies.
Make predicates sargable: a function or implicit cast on an indexed column (WHERE YEAR(created_at) = 2024, LOWER(email) = ...) or a leading-wildcard LIKE '%x' prevents a B-tree seek. Rewrite as a range or add an expression index. Also select only the needed columns, replace correlated subqueries with joins or EXISTS, and use keyset pagination instead of deep OFFSET.
Look beyond the SQL: N+1 query loops in application code, lock waits behind another transaction, and heavy reports that belong in a summary table or materialized view. Confirm the fix by comparing before-and-after plans and timings on production-sized data.

Follow-up

An index exists on the filtered column but the planner ignores it. What are the possible reasons?
How would you speed up a nightly report that aggregates hundreds of millions of rows?
What does adding three indexes do to a table that receives heavy batch inserts?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
A dashboard runs this for 40 owners in one page load. What changes about the design?

What are the primary differences between relational and non-relational databases, and how do you decide which

Medium · Technical & Conceptual Foundations

What are the primary differences between relational and non-relational databases, and how do you decide which to use?

Approach

Relational: tables with a declared schema, SQL with joins, constraints (primary key, foreign key, unique, check) enforced by the engine, and ACID transactions across rows and tables. Non-relational is several families, each built around one access pattern: document (MongoDB), key-value (Redis, DynamoDB), wide-column (Cassandra) and graph (Neo4j).
Trade-offs to name: with a relational engine the database enforces integrity, while a schema-flexible store pushes validation into application code. Many NoSQL stores partition by key natively and scale writes horizontally, but cross-partition joins and multi-record transactions are limited or costly, and some default to eventual or tunable consistency.
Decide from access patterns and integrity needs, not fashion. Orders, billing and job records that need joins, reporting and exact consistency belong in a relational database. A key-value store suits caching and session data. A document store suits per-client records whose shape varies and that are always read whole.
Mention the middle ground: PostgreSQL JSONB (with a GIN index) holds variable per-client attributes next to relational columns, which often removes the reason to run a second database. Default to relational unless a concrete requirement such as write scale, schema variability or key-lookup latency rules it out.

Follow-up

How would you store client-specific fields that differ for every client in PostgreSQL?
What does eventual consistency mean for a reader who queries right after a write?
If a relational table outgrows one server, what are your options before switching databases?

How would you write a script to parse a large, unstructured text file and convert it into a structured format

Medium · Workflow Automation & Scripting

How would you write a script to parse a large, unstructured text file and convert it into a structured format like JSON or XML?

Approach

Pin down the input before coding: get real sample files and find the record boundaries (one record per line, blank-line separated, header markers, fixed-width columns), the encoding (UTF-8, cp1252, BOM) and the size. Write the target schema first, so every parsed field has a destination and a type.
Stream it: iterate line by line (for line in open(path, encoding=...)) and hold only the current record. That keeps time at O(n) and memory proportional to one record instead of the file. For multi-line records, use a small state machine that emits a record when it reaches the next boundary or end of file.
Extract fields with regexes compiled once, using named groups, or with slicing for fixed-width layouts. Emit JSON Lines (one object per line) so output also streams. Always serialize with json.dumps or an XML library, never string concatenation, so quotes, ampersands and angle brackets are escaped correctly.
Handle bad input without killing the run: write each unparseable record to a reject file with its line number and reason, count the rejects, and fail the job if the reject rate passes a threshold. Validate the output against a JSON Schema or XSD before handing it downstream.
Test on fixtures that cover an empty file, a final record with no terminator, unexpected encoding, stray whitespace and very long lines. For speed, profile first, then split the file at record boundaries and parse the chunks in separate processes.

Follow-up

The output must be one valid JSON array rather than JSON Lines. How do you produce it without holding everything in memory?
If you split the file for parallel parsing, how do you avoid cutting a record in half?
How would someone downstream know which input records were dropped, and why?

What strategies do you use to automate repetitive command-line tasks in a Windows or Linux environment?

Medium · Workflow Automation & Scripting

Approach

Pick the language for the platform. Bash on Linux with set -euo pipefail, every variable quoted and a trap for cleanup. PowerShell on Windows with $ErrorActionPreference = 'Stop' and try/catch. Python for anything that must run on both, using pathlib and subprocess.run with a list of arguments and check=True instead of shell=True.
Trigger it properly: cron or systemd timers on Linux, Task Scheduler on Windows, or event-driven watchers (inotifywait, Python watchdog, .NET FileSystemWatcher). A watcher can fire before the writer has finished, so wait for the file size to stop changing or agree on a write-then-rename or marker-file convention.
Make every run safe to repeat: check state before acting, move processed files to an archive folder, take a lock (flock, or a lock file on Windows) so overlapping runs cannot double-process, and offer a dry-run flag.
Make failures visible: timestamped logs, a non-zero exit code on any failure, and an alert when a run fails or does not run at all. Pass paths and settings through arguments or a config file, keep credentials out of the script, and keep the scripts in version control with a test on sample data.

Follow-up

A scheduled job sometimes overlaps with the previous run. How do you prevent double processing?
How does your script avoid picking up a file that is still being copied in?
How would you notice that a nightly job silently stopped running?

How do you ensure data integrity and security when transferring sensitive customer files between systems?

Medium · Workflow Automation & Scripting

Approach

Encrypt in transit with SFTP (runs over SSH) or FTPS (FTP over TLS), never plain FTP. They are different protocols. For SFTP, use key-based authentication, pin and verify the server's host key, and give each partner a least-privilege account confined to its own directory.
Encrypt at rest as well: file-level PGP/GPG encryption keeps the data protected on every intermediate server and disk, and a PGP signature proves who sent it. Keep keys and passwords in a secrets manager or KMS, never in scripts, and rotate them on a schedule.
Prove integrity: the sender publishes a SHA-256 checksum plus a manifest or trailer with the record count and control totals. The receiver recomputes and compares before processing, which catches corruption and truncation. MD5 detects accidental damage but not deliberate tampering.
Hand off atomically: upload under a temporary name and rename once complete, or write a .done marker, so the consumer never reads a partial file. Record each file's hash to reject duplicate deliveries, so a resend is not processed twice.
Audit and clean up: log who sent what, when, with what result (no customer data in the logs), alert on failed or missing transfers, and purge files after the agreed retention period.

Follow-up

What is the difference between SFTP and FTPS, and which firewall issues come with each?
With no checksum from the sender, how could you still detect a truncated file?
How would you rotate a PGP key without breaking a partner's scheduled transfers?

Explain the concept of variable data printing (VDP) and how software tools manage dynamic layouts.

Medium · Workflow Automation & Scripting

Approach

Define it: variable data printing merges a data source (one record per recipient) with a template, so every piece carries its own text, offers, images and barcodes while static elements such as logos and backgrounds repeat. The data drives the content, and rules decide what each record gets.
Explain dynamic layout. Business rules pick content per record (language version, offer tier, insert pages). Copyfitting shrinks text within set limits or reflows it when a name or address is long. Variable-length tables flow across pages, so each record's page count can change, which affects duplexing, imposition and finishing.
Name the pipeline: data intake and validation (missing fields, address checks, encoding), a composition engine that applies template and rules (the role requirements candidates describe list tools such as Quadient Inspire and HP SmartStream), then output as print-ready PDF or a print stream, plus a digital version where needed.
Cover high-volume output: formats such as PDF/VT (ISO 16612-2) let the RIP cache reusable static objects instead of re-rendering them for every record, which ordinary page-by-page PDF does not express as cleanly. Batch and split jobs for parallel rendering.
Show production safety: give each record a sequence ID printed on the piece (often in a barcode) so missing or damaged pieces can be reprinted, and reconcile input record count, output piece count and pages per record before release.

Follow-up

A customer's name and address overflow the window envelope area. What should the composition logic do?
How would you prove that every input record produced exactly one output piece?
Why does a format that caches reusable objects matter when a job has a million records?

p99 jumped on one listing filter while p50 stayed flat

Medium · query planning · composite index · percentiles

After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.

Approach

Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.

Follow-up

The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?

Suggested practice checklist

The plan follows the three reported rounds: aptitude and core coding first, then data and scripting, database work and workflow design, then group discussion practice and personal interview stories. Each day ends with something you can show.

Day 1 — Map the loop and set a baseline

Write the three reported rounds in order and, for each, one line on what you must show and how ready you are
Take a timed set of logic, pattern and mathematical word problems and note the type that is slowest
Choose the one scripting language you will use for every coding answer, and list the data formats the role mentions (XML, JSON, CSV, PDF) with your current comfort in each

Deliverable: A one-page round map, a scored aptitude baseline and your chosen language with a gap list for the data formats.

Day 2 — Core coding and conceptual foundations

Write a function that finds duplicate records in a very large dataset: an in-memory hash set version, then a streaming or partitioned version, with time and memory costs for each
Explain a hash table (hashing, collisions, resizing, average and worst-case lookup) out loud in under two minutes
Explain synchronous versus asynchronous work with one example where each fits a file-processing task

Deliverable: Two working duplicate-detection functions with complexity notes, plus two spoken explanations you can deliver without notes.

Day 3 — Parsing, validation and regular expressions

Write a script that reads a large unstructured text file line by line and emits JSON, then XML, without loading the file into memory
Add input handling: skip and record malformed lines with their line numbers, and test it with empty lines and null-like values
Write three validation patterns, each with a table of valid, invalid and boundary strings, and prepare a first-person story about using regex to validate data

Deliverable: A working parse-and-convert script, a rejects file from a deliberately dirty test input, and a pattern test table.

Day 4 — Databases and slow queries

Write your decision rule for relational versus non-relational storage, with one example of each from a file-processing system
Take a slow query and walk the tuning order: read the execution plan, check which index is used, and check the joins and filters

Deliverable: A tuning checklist in the order you would apply it and a spoken answer for each of the two database questions.

Day 5 — Automation workflows and production safety

Sketch the reported workflow scenario on paper: a monitored secure folder receives CSV files, they are validated, and a composition step is triggered, with the failure path for each stage
Write down how you would automate a repeated command-line task on Windows (PowerShell) and on Linux (shell and cron), and how you would check it ran
Prepare an answer on keeping files intact and private in transfer: encrypted channel, checksums, access limits, and no sensitive values in logs
Explain variable data printing in plain words, as a template merged with a record, and practise the error logging and exception handling answer with a real script as your example

Deliverable: A workflow diagram with failure paths, a one-paragraph transfer checklist and a spoken VDP explanation.

Day 6 — Group discussion rehearsal

Run the client-portal backlog scenario with two or three people, using stated criteria before ranking
Run the print-production outage scenario in the same group, and record both sessions
Review the recordings for how much you spoke, whether you summarised, and whether you drew in quieter people, then write your answer for prioritising multiple urgent requests

Deliverable: Notes from two recorded sessions with three things to change, plus a written prioritisation method with one example.

Day 7 — Personal interview rehearsal

Write four stories in situation, action, result form: the production bug under a deadline, code-review feedback, cross-functional delivery, and your own automation project
Outline two projects you can explain at any depth, including data formats, failure handling and a measurable result
Run a full mock: one project walk-through, one technical scenario and the four stories, aloud and in first person

Deliverable: A one-page story sheet, two project outlines and a recorded mock run with notes.

Behavioral and collaboration preparation

Candidates report that the personal interview covers past projects alongside HR questions, and the group discussion is a behavioral test in itself. For this role the stories that matter are about keeping production work correct under pressure: handling bad data, fixing a failure quickly, taking review feedback, and working with project managers and production staff. Prepare each story with your own decision, the effect on the work, and what you would change.

How do you handle error logging and exception handling in a production-grade script?

Medium · Technical & Conceptual Foundations

Approach

Open with a concrete script you ran in production, then walk through its failure design: log at levels (info for progress, warning for a skipped record, error for a failed step), and put context in every message such as job name, input file, record number and the offending value.
Catch specific exceptions, never a bare catch that swallows the error. Separate a bad record (quarantine it with its line number, write it to a rejects file, keep going) from a fatal condition (stop, return a non-zero exit code, alert), and say where you drew that line and why.
Retry only transient failures such as a locked file or a dropped connection, with a capped backoff, and make reruns safe by writing output to a temporary name and renaming on success, so a half-written file is never picked up downstream.
Keep sensitive customer data out of logs by logging identifiers and field names, not values. Rotate logs, and end with how you would find the cause of a failure from the log alone the next morning.

Follow-up

A file has one malformed row among millions: do you fail the run or skip it, and how does anyone find out?
How do you stop a log from leaking personal data while keeping it useful for debugging?
How would you make the script safe to rerun after a crash halfway through?

Describe your experience with regular expressions (Regex) and how you use them to validate complex data inputs

Medium · Workflow Automation & Scripting

Describe your experience with regular expressions (Regex) and how you use them to validate complex data inputs.

Approach

Tell it as a real use: name the input you validated (for example postal codes, dates, account or reference numbers, file names), the pattern you wrote, and the bad inputs it stopped. Use anchors or a full-match call so a valid substring does not pass an invalid whole value.
Show that you validate in layers: a regex checks the shape, then code checks meaning (a real calendar date, a checksum such as Luhn, a value within range). Say regex alone cannot tell that month 13 is wrong without help.
Mention readability and testing: named groups, verbose mode with comments, and a table of valid, invalid and boundary cases (empty string, trailing whitespace, Unicode characters, very long input) kept as unit tests.
Show you know the limits: nested quantifiers can backtrack catastrophically on crafted input, and regex is the wrong tool for nested formats like XML or JSON, where a parser belongs. The mistake that sinks the answer is claiming regex as the solution to everything.

Follow-up

How would you spot and fix a pattern that backtracks badly on long input?
When would you refuse to use a regex and use a parser or a library instead?
How do you test a validation pattern so a change to it cannot quietly let bad data through?

Describe a time when you had to resolve a critical production bug under a tight deadline. What was your approa

Medium · Behavioral & Collaboration

Describe a time when you had to resolve a critical production bug under a tight deadline. What was your approach?

Approach

Choose an incident where you made the calls. Open with two sentences of situation: what broke, who or what it affected, and the deadline. Spend the rest on the order of your actions.
Show the sequence: contain first (roll back, pause the queue, or hold the affected batch), then reproduce with the smallest failing input, narrow the cause using logs and recent changes, fix minimally, and verify against the real failing case before release.
Quantify the blast radius. Say how many records or runs were affected, how you worked out which ones, and what happened to output that had already gone out, because stopping the bleeding and cleaning up are separate tasks.
Cover communication (who you told, how often, in plain terms) and the follow-up: the root cause, the regression test you added and any guard against the same bad input. The story fails when it ends at 'it was fixed' without a cause or a prevention step.

Follow-up

How did you decide between rolling back and shipping a forward fix?
What did you tell non-technical stakeholders while it was still broken?
What change after the incident would have caught it earlier?

How do you handle constructive feedback from a peer or senior engineer during a code review?

Medium · Behavioral & Collaboration

Approach

Pick a specific review with real disagreement or a significant request, not a generic claim that you welcome feedback. State what the reviewer asked for and what your first reaction was.
Show the process: ask a clarifying question until you understand the concern, test it against evidence (a failing test, a benchmark, the style guide), then either make the change and say what you learned, or reply with data and an alternative if you still disagree.
Show a case where you were wrong and changed your approach, and one where you pushed back respectfully and the outcome was fair. Answering 'I always accept every comment' sounds like you do not think about the code.
Close by describing how you give reviews too: comments on code not person, a reason with each request, and a separation of blocking issues from preferences.

Follow-up

Tell me about a time you disagreed with a reviewer and how it ended.
What do you do when two senior reviewers give conflicting feedback?
How do you review a colleague's script that works but is hard to maintain?

Tell me about a time when you had to collaborate with a cross-functional team to deliver a project.

Medium · Behavioral & Collaboration

Approach

Pick a project with named functions around you, for example project management, account or client-facing staff and production or operations people, and state what each needed from the result. Candidates describe this role as working with those groups, so a story of turning a client requirement into an automated pipeline fits.
Describe your part precisely: how you turned a loose request into written requirements and acceptance cases (sample inputs, expected outputs, error handling), and how you confirmed them with each group before building.
Show a point of friction, such as conflicting priorities or a change late in the project, and how you resolved it: explaining the trade-off in plain language, agreeing a smaller first release, or moving a date with everyone's knowledge.
End with the delivered result and what you learned about working across functions. Avoid a story in which the team did everything and your own role is unclear.

Follow-up

How did you explain a technical limit to someone without a technical background?
What changed in the requirements midway, and how did you handle it?
What would you do differently in how you kept the other teams informed?

How do you prioritize your tasks when working on multiple high-priority development requests?

Medium · Behavioral & Collaboration

Approach

Lay out your method first: list every request with its deadline, who is affected, what happens if it slips, and its dependencies; production-down issues come before planned feature work, and small quick tasks can be batched.
When two requests really conflict, say you take the question back to the requesters or your manager so the business decides the order, and you present the trade-off clearly instead of silently choosing.
Make it concrete with one example: the requests, the order you chose and why, what you postponed and how you told that person, and what happened after.
Include how you protect quality under pressure by keeping tests and validation in place, and how you handle an urgent request that arrives mid-task. The answer that sinks is 'I work longer and do everything', which shows no judgement and no communication.

Follow-up

Two stakeholders each say their request comes first. What do you do?
What did you decide not to do, and how did you tell the person affected?
How do you handle a new urgent request when you are halfway through another?