Atlanticus Services Software Engineer Interview Guide

The 60-second brief

Atlanticus Services works in financial services, and candidates describe the Software Engineer role as software for application fraud analysis, with identity fraud named specifically. The systems involved process financial transactions and manage data-heavy interactions, so correctness and security matter alongside delivery. Day to day, candidates report building and maintaining applications, turning requirements from product managers and designers into technical specifications, and reviewing teammates' code. Data analysts and cybersecurity staff are listed as regular collaborators. The skills listed for the role are C#, SQL, ADO.NET and application frameworks, with cloud computing, DevOps, data analysis tools and agile practice named as extras.

This guide is for engineers preparing for the Software Engineer loop at Atlanticus Services. It covers the four stages candidates report, the C#, ADO.NET, OOP and SQL questions, the two coding questions (cycle detection in a linked list and finding two numbers that add up to a target), the debugging and fraud-feature case questions, and the behavioral prompts. Each reported question has specific preparation notes, and a seven-day plan sequences them.

Candidates report four stages over roughly 3-5 weeks: a Screening Call, Technical Interviews, Behavioral Questions and Case Studies. Emphasis varies by team, so treat the stage list as a shape for your preparation rather than a fixed script.

Focus areas

• C#
• SQL
• Identity Fraud

Overview

The reported stack is narrow, which helps you. C#, ADO.NET and SQL come up repeatedly, along with object-oriented design, so depth in those four beats a wide survey of other tools. The reported technical questions ask what distinguishes C# from other languages, how ADO.NET works and why it matters for data access, which OOP principles you value in design, and how you manage SQL transactions and what goes wrong with them. A strong answer to each names concrete types and mechanisms, such as SqlConnection, SqlCommand, SqlDataReader, isolation levels and deadlocks, rather than reciting definitions.

The coding questions are classic and short: detect a cycle in a linked list, find two numbers in a list that add up to a target, and tune a slow SQL query. They reward clean, correct code with the complexity stated, and a tuning answer that starts from the execution plan instead of guessing at indexes. Practise writing them in C#, since that is the language the role lists, and say the time and space cost of each approach out loud.

The domain shows up in the case-style questions. Candidates report one about debugging a system that fails intermittently and one about designing a new fraud-detection feature and making sure it works. For the fraud question, effectiveness means measurable outcomes: catching bad applications while not blocking legitimate applicants, testing against historical data, and watching the feature after release. For the debugging question, a repeatable sequence of narrowing the failure matters more than a lucky guess.

The behavioral prompts reported are about a challenging team dynamic, prioritizing across several projects, and improving a process on your own initiative. Because the role is described as working with product managers, designers, data analysts and cybersecurity staff, choose stories that cross those boundaries and show a decision you made, not just a result you witnessed.

The Atlanticus Services interview process

These are the four stages candidates describe, in the order they report them. The question lists in this guide are grouped by type, so use them to prepare for each stage without expecting a particular question in a particular round.

Screening Call

Candidates describe the Screening Call as a first conversation about general fit, with a few basic technical questions. It is the stage to show that your background lines up with the listed stack of C#, SQL and ADO.NET and that you understand the role involves fraud analysis in financial services. Keep answers short and concrete, and be ready to expand on any technology you mention on your CV.

What to demonstrate

• General fit for a Software Engineer role in financial services, as candidates describe the call
• Basic grounding in C# and SQL, the core skills listed for the role
• How clearly you summarise your own recent work

How to prepare

• Write a short spoken summary of your last two projects that names where you used C#, SQL or ADO.NET and what you personally owned.
• Rehearse quick, accurate definitions: what makes C# distinct, what ADO.NET is, what a database transaction guarantees.
• Prepare one honest sentence on why fraud and identity protection in financial software interests you, tied to something you have built.

Technical Interviews

The Technical Interviews are reported as a series of sessions that go deeper into coding ability and problem-solving. Candidate accounts tie technical proficiency to C#, SQL, ADO.NET and object-oriented design, and the reported coding questions are cycle detection in a linked list, a two-sum style search, and tuning a slow SQL query. Write code in C# where you can, talk through your reasoning as you go, and state complexity for every solution.

What to demonstrate

• Working code in C# and correct SQL
• Command of ADO.NET and object-oriented principles
• Algorithmic knowledge and the ability to explain complexity
• Problem-solving approach, including how you reason aloud

How to prepare

• Implement linked-list cycle detection two ways in C#, with a hash set and with two pointers, and state the space cost of each.
• Solve the two-numbers-add-to-a-target problem with a Dictionary and again with sorted two pointers, then handle duplicates and the same element used twice.
• Walk through a slow-query investigation: read the plan, find the scan, fix the predicate or index, and re-check the plan.
• Explain ADO.NET end to end: connection, command, parameters, reader versus adapter, pooling, and disposal.

Behavioral Questions

The Behavioral Questions stage is reported as an assessment of culture alignment and collaboration skills. The reported prompts cover a challenging team dynamic, prioritizing across multiple projects, and taking initiative to improve a process. Candidate guidance for this stage is to structure answers with Situation, Task, Action and Result, and the role's collaborators (product managers, designers, data analysts, cybersecurity staff) are good material for stories.

What to demonstrate

• Collaboration and communication with cross-functional teammates, as the stage is described
• Alignment with team and ethical working practices
• How you structure and own a story from situation to result

How to prepare

• Write three STAR stories from your own work and map each to the three reported prompts, noting which decision was yours.
• Add specifics to every story: the people or roles involved, the alternatives you weighed, and a result you can back with a real before-and-after.
• Prepare one story about a code review or cross-team disagreement, since reviews and collaboration are part of the role as candidates describe it.

Case Studies

The Case Studies stage is reported as real-world challenges that test user-centric design and a sense of broader impact. The case-style questions candidates report are debugging a system that fails intermittently and designing a new feature for fraud detection and making sure it is effective. Work through each as a short structured walk-through: clarify the goal, lay out the steps, say how you would measure success, and name what could go wrong.

What to demonstrate

• Structured problem-solving on an open-ended scenario
• User-centric thinking: what the change means for the people who apply and the people who review
• Awareness of broader impact in a fraud and financial setting

How to prepare

• Rehearse an intermittent-failure walk-through: confirm the symptom, find what changed, correlate with logs and load, reproduce, then fix and add monitoring.
• Outline a fraud-feature design on one page: signals, data sources, decision path, false-positive handling, testing on historical data, rollout and monitoring.
• Practise saying who is affected by each design choice, including legitimate applicants who could be wrongly flagged and the analysts who review flags.

Where candidates lose points

Answering the C# and ADO.NET questions with definitions only

For C#, pick four or five features that matter in practice (managed memory, static typing, LINQ, async and await, properties and delegates) and show a short snippet for each. For ADO.NET, name the connected types (SqlConnection, SqlCommand, SqlDataReader), the disconnected ones (SqlDataAdapter, DataSet), parameterized queries against SQL injection, connection pooling and using blocks for disposal.

Describing SQL transactions as just BEGIN and COMMIT

Cover atomicity and rollback on error, isolation levels and the anomalies each one allows, how long-held locks cause blocking, how deadlocks arise and get retried, and how a transaction maps to SqlTransaction or TransactionScope in C#. Keep transactions short and give one pitfall you have met.

Jumping to a nested loop or a hash set for the cycle and two-sum questions and stopping there

State the brute-force cost first, then improve. For the linked list, show the hash set and then the two-pointer method with constant space, and say how to find where the cycle starts. For two-sum, use a Dictionary of seen values, and handle duplicates, negatives, no valid pair and reusing the same element.

Designing the fraud feature without saying how you would know it works

After the signals and data flow, define success: how many fraudulent applications it catches against how many legitimate applicants it blocks, how you would test it on historical applications, a limited rollout that runs alongside the existing process, and monitoring for drift and analyst feedback.

Telling behavioral stories in terms of the team's outcome with no visible personal decision

Rewrite each STAR story so the Action section lists your own steps in the first person singular, names the alternative you rejected, and ends with a result you could verify. Cut any story where you only observed.

Questions & practice

Write a function to detect cycles in a linked list.

Medium · Coding / Algorithms

Approach

1. State the baseline first: walk the list and store visited nodes in a HashSet; a repeat means a cycle. It is O(n) time and O(n) space, and in C# HashSet<ListNode> compares by reference unless the class overrides Equals, which is what you want here.
2. Then give Floyd's tortoise and hare: slow moves one step, fast moves two; if fast reaches null there is no cycle, and if slow == fast they are in a cycle. O(n) time, O(1) extra space.
3. Guard the loop correctly: while (fast != null && fast.next != null), so an empty list, a single node and an even-length list all end without a null dereference; a single node pointing to itself must return true.
4. Extend it if asked: to find where the cycle starts, reset one pointer to head after they meet and advance both one step at a time; they meet at the entry node. To get the cycle length, hold one pointer fixed and count steps until the other returns.
5. Test aloud on small cases: null head, one node without a loop, one node with a self-loop, a two-node cycle, and a long tail before the loop.

Follow-up

• Return the node where the cycle begins, and explain why resetting one pointer to the head finds it.
• Can you solve it without modifying the list and without extra memory? What if you were allowed to modify it?
• How would you detect a cycle in a directed graph rather than a linked list?

Given a list of integers, find two numbers such that they add up to a specific target.

Medium · Coding / Algorithms

Approach

1. Clarify the contract: return indices or values, exactly one answer or possibly none, can the same element be used twice, and is the input sorted. Then give the brute force: check every pair, O(n^2) time, O(1) space.
2. Optimal one-pass solution: a Dictionary<int, int> from value to index; for each element x, check whether target - x is already in the map before inserting x. O(n) time, O(n) space.
3. Checking before inserting matters: it stops one element pairing with itself, and it still handles duplicates such as [3, 3] with target 6, because the first 3 is in the map when the second is read.
4. If the array is sorted, or sorting is allowed and only values are needed, use two pointers from both ends: move left up if the sum is too small, right down if too large. O(n) after an O(n log n) sort with O(1) extra space, but sorting loses the original indices unless you sort (value, index) pairs.
5. Edge cases to say aloud: fewer than two elements, no valid pair (return an empty result or throw, as agreed), negative numbers, and int overflow in target - x in C#, which you can avoid by computing in long.

Follow-up

• Return all unique pairs that sum to the target, without duplicates.
• Extend it to three numbers that sum to the target. What is the best complexity you can get?
• How would you answer it if the numbers arrive as a stream too large to keep in memory?

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.

Approach

1. Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
2. Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
3. State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
4. Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.

Follow-up

• The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
• A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

How do you manage database transactions in SQL, and what are the potential pitfalls?

Medium · Technical / Domain Questions

Approach

1. Start with the mechanics: BEGIN TRANSACTION, the statements, then COMMIT, or ROLLBACK on error, so the unit is all-or-nothing (ACID). In SQL Server, wrap it in TRY...CATCH with SET XACT_ABORT ON, and in the CATCH roll back only if @@TRANCOUNT > 0.
2. Explain isolation levels and the anomaly each one prevents: READ UNCOMMITTED allows dirty reads, READ COMMITTED (SQL Server's default) prevents them, REPEATABLE READ prevents non-repeatable reads, SERIALIZABLE also prevents phantoms; SNAPSHOT or READ_COMMITTED_SNAPSHOT uses row versions so readers do not block writers.
3. Pitfall one, long transactions: they hold locks and block other sessions, so keep them short and never wait on a network call or user input while a transaction is open.
4. Pitfall two, deadlocks: two sessions take locks in opposite order and one becomes the victim (error 1205 in SQL Server); prevent them by touching tables in a consistent order and keeping transactions small, and retry the victim.
5. Pitfall three, lost updates and nesting: read-then-write without UPDLOCK or a rowversion check lets two writers overwrite each other, and in SQL Server an inner COMMIT only decrements @@TRANCOUNT while any ROLLBACK undoes the whole outer transaction unless you use a savepoint.

Follow-up

• Which isolation level would you use for a transfer between two balances, and what does it cost in concurrency?
• How would you implement optimistic concurrency with a rowversion column, and what does the application do when the check fails?
• How do you find out which queries were involved in a deadlock?

How would you optimize a SQL query that is running slowly?

Medium · Coding / Algorithms

Approach

1. Measure before changing anything: get the actual execution plan and the I/O numbers (in SQL Server, the actual plan plus SET STATISTICS IO, TIME ON; in PostgreSQL, EXPLAIN (ANALYZE, BUFFERS)), and confirm the query is slow every time, not only for certain parameters.
2. Read the plan for the usual culprits: table or index scans where a seek is expected, key lookups repeated for many rows, large differences between estimated and actual rows (stale statistics), sorts or hash joins spilling to disk, and implicit conversions on a join or filter column.
3. Fix the predicates so they can use an index: replace YEAR(created_at) = 2024 with a date range, avoid a leading-wildcard LIKE, and match parameter types to column types so no conversion is applied to the column.
4. Design the index for the query: equality columns first, then the range column, and INCLUDE the selected columns to make it covering; state the trade-off that each index slows inserts and updates and uses storage.
5. Also cover non-index fixes: select only needed columns, filter before joining, update statistics, and in SQL Server consider parameter sniffing when one cached plan suits some parameters and not others. Re-measure with the same inputs to confirm the gain.

Follow-up

• The query is fast for one customer and slow for another with the same plan cache. What is happening and how do you fix it?
• You added an index and the optimizer still does not use it. What do you check?
• How would you speed up a slow report query when you are not allowed to add indexes to the table?

What are the key features of C# that differentiate it from other programming languages?

Medium · Technical / Domain Questions

Approach

1. Pick a comparison point (Java and C++ are the usual ones) and list concrete differences, not adjectives: C# runs on the CLR with JIT compilation and garbage collection, but unlike Java it has user-defined value types (struct) that live inline without a separate heap object.
2. Name the language features with an example each: reified generics (List<int> stores ints with no boxing, unlike Java's type erasure), properties and events, delegates and lambdas, extension methods, and LINQ for querying collections and databases with one syntax.
3. Cover async/await: the compiler rewrites an async method into a state machine that resumes when the awaited Task completes, so I/O-bound code such as database calls does not block a thread.
4. Mention the newer safety and modelling features: nullable reference types (C# 8) turn possible null dereferences into compiler warnings, records give value equality for immutable data, and pattern matching with switch expressions replaces type-check-and-cast chains.
5. Close with resource handling: IDisposable plus a using statement gives deterministic cleanup of connections and file handles, which the garbage collector alone does not.

Follow-up

• When would you choose a struct over a class, and what goes wrong with a large mutable struct?
• What is the difference between IEnumerable<T> and IQueryable<T> in LINQ, and when does a query actually execute?
• What happens if you call .Result on a Task inside an async call chain?

Can you explain how ADO.NET works and its importance in data access?

Medium · Technical / Domain Questions

Approach

1. Split ADO.NET into its two layers: the connected layer (SqlConnection, SqlCommand, SqlDataReader, SqlParameter) streams rows from an open connection, while the disconnected layer (SqlDataAdapter filling a DataSet or DataTable) loads data into memory and closes the connection.
2. Walk through the call sequence: open the connection inside a using block, create a command with SqlParameter objects, then call ExecuteReader for rows, ExecuteScalar for a single value or ExecuteNonQuery for INSERT/UPDATE/DELETE and its affected-row count; prefer the Async variants for I/O.
3. Explain why parameters matter: they keep user input out of the SQL text, which prevents SQL injection and lets the server reuse a cached plan; never build the statement with string concatenation.
4. Explain connection pooling: SqlClient pools connections per distinct connection string and is on by default, so Dispose returns the connection to the pool; leaked connections exhaust it (default Max Pool Size is 100) and show up as timeouts under load.
5. Give its place today: Entity Framework Core and Dapper both run on ADO.NET providers, so knowing ADO.NET explains what an ORM generates, how transactions attach (connection.BeginTransaction and assigning command.Transaction), and where to drop down for a hot path.

Follow-up

• When would you use a SqlDataReader instead of a DataSet, and what does the reader hold open while you iterate?
• How do you run two commands in one transaction with ADO.NET, and what happens if you forget to set command.Transaction?
• Why might you choose raw ADO.NET or Dapper over Entity Framework Core for a specific query?

What principles of object-oriented programming do you find most valuable in software design?

Medium · Technical / Domain Questions

Approach

1. Name the four pillars briefly (encapsulation, abstraction, inheritance, polymorphism), then choose the two or three you actually rely on and say why; a ranked opinion with reasons beats a recited list.
2. Make encapsulation concrete: an object guards its invariants, for example an account whose balance can change only through methods that validate the amount, never through a public setter.
3. Argue for programming to interfaces and composition over inheritance: small interfaces with several implementations (for example a set of validation rules behind one interface) let me add behaviour without editing existing classes, which is the open/closed principle in practice.
4. Tie dependency inversion to testability: depending on an interface injected through the constructor lets a unit test substitute a fake repository instead of a real database.
5. Show judgement about limits: deep inheritance hierarchies create fragile base classes, and a subclass that breaks its parent's contract violates Liskov substitution (the Square deriving from Rectangle example).

Follow-up

• In C#, when would you use an abstract class rather than an interface, given that interfaces can have default methods since C# 8?
• Give an example from your own code where inheritance caused a problem and how you refactored it.
• How does dependency injection change how you write unit tests?

Describe a complex problem you solved using a technical solution.

Medium · Technical / Domain Questions

Approach

1. Pick one problem with a hard technical constraint you can state in a sentence (latency, data volume, correctness, a failing integration), and make sure you were the person who chose the solution.
2. Explain the root cause before the fix: what you measured or reproduced, and which hypothesis the evidence ruled out.
3. Name the two or three options you considered and the trade-off that decided it, for example caching versus query rewriting versus a schema change, with the cost of each.
4. Describe the mechanism of the fix technically enough that a follow-up can go a level deeper (the index you added, the algorithm you swapped, the concurrency bug you closed), then give a measured result: before and after numbers, error rate, or time saved.
5. End with what you would do differently and what you did to stop the problem recurring (a test, an alert, a runbook). The story sinks if the problem is vague or the result is unmeasured.

Follow-up

• Why did you reject the alternative you mentioned, and would you choose differently today?
• How did you verify that your change caused the improvement and not something else?
• What broke or got harder because of your solution?

If you were to design a new feature for fraud detection, what steps would you take to ensure its effectiveness

Medium · Problem-Solving / Case Studies

If you were to design a new feature for fraud detection, what steps would you take to ensure its effectiveness?

Approach

1. Define the fraud pattern and the decision first: which type (for example identity fraud on new applications, using stolen or synthetic identities), where in the flow the check runs, and what it does: approve, decline, step up verification, or send to manual review.
2. Choose success metrics before building: fraud caught (recall) and fraud losses avoided, false positive rate and the good customers declined or delayed, review queue volume, and added latency at the decision point. Say how a false positive is priced against a missed fraud.
3. Describe the signals and the mechanism: velocity checks such as how many applications share an SSN, phone, email, address or device in a time window, mismatches between identity elements, and verification data. Start with explainable rules and add a model score once enough labelled data exists.
4. Handle labels and evaluation: confirmed fraud labels arrive weeks later through charge-offs and investigations, so backtest on historical applications, then run the feature in shadow mode (score and log without acting) before turning it on for a holdout or a share of traffic.
5. Plan for operation: monitor score distributions and catch rates for drift, feed review outcomes back as labels, expect fraudsters to adapt once a rule is visible, and keep decision reasons explainable, since a declined credit application can require a stated reason.

Follow-up

• How would you set the decision threshold, and how does it change if manual review capacity is fixed?
• How do you measure the feature's effectiveness when fraud labels arrive months after the decision?
• What would you do if the feature raised the decline rate for legitimate applicants?

How would you approach debugging a system that is intermittently failing?

Medium · Problem-Solving / Case Studies

Approach

1. Characterise the failure before guessing: how often, since when, which endpoints, hosts, inputs or times of day, and the exact error. Correlate logs, metrics and traces by request or correlation id to find what the failing requests have in common.
2. Work through the usual causes of intermittent failures: a single unhealthy instance behind a load balancer, resource exhaustion (database connection pool, thread pool starvation, memory), timeouts or rate limits from a dependency, race conditions on shared state, specific data values, and anything time-dependent such as cache expiry, scheduled jobs or time zones.
3. Check what changed near the first occurrence (deploys, config, traffic volume, dependency versions) and bisect if a change lines up, while treating timing as a lead, not proof.
4. Reproduce under the suspected conditions: replay the failing inputs, run a load or concurrency test, or add targeted logging around the failing path if it cannot be reproduced locally.
5. Verify the fix against the base rate: if the failure rate is p, you need about 3/p clean trials for roughly 95% confidence that the fix worked (the rule of three), so compare error rates over a long enough window rather than one quiet hour.

Follow-up

• It never fails locally or in staging, only in production. What do you do next?
• The failures appear only under peak load. Which metrics do you check first and what would each tell you?
• How do you tell a real cause from a coincidence such as a deploy that happened the same day?

Suggested practice checklist

Seven days built around the reported stages: first mapping your gaps, then C# and OOP, ADO.NET and transactions, coding, SQL tuning and debugging, the fraud case, and finally behavioral stories with a full mock run.

Day 1 — Map the four stages and rate your gaps

• Write the four reported stages (Screening Call, Technical Interviews, Behavioral Questions, Case Studies) and list under each the question types you expect to practise.
• Rate yourself on C# features, OOP, ADO.NET, SQL transactions, SQL tuning, linked lists, hash maps, debugging and fraud design, and mark the two weakest.
• Write the short spoken summary of your recent work that names where you used C#, SQL or ADO.NET.

Deliverable: A one-page stage map, a skills rating grid with two gaps marked, and a written spoken summary of your recent work.

Day 2 — C# features and OOP principles

• Write short C# snippets for five features that distinguish the language (for example async and await, LINQ, properties, delegates and events, garbage-collected memory) and explain why each matters.
• Explain encapsulation, inheritance, polymorphism and abstraction with one example, such as an identity-check interface with two implementations, and say which principle you value most and why.

Deliverable: Five annotated C# snippets, one OOP example with its interface, and spoken answers to the C# features question and the OOP principles question.

Day 3 — ADO.NET and database transactions

• Write a C# method that reads rows with SqlConnection, SqlCommand with parameters and SqlDataReader inside using blocks.
• Extend it to update two tables inside a SqlTransaction with rollback on exception.
• Write a list of transaction pitfalls: isolation levels, blocking, deadlocks, long-running transactions and forgetting to roll back, with one fix for each.

Deliverable: A working C# data-access method with a transaction, plus a one-page pitfalls list that doubles as the spoken answer to both questions.

Day 4 — Linked-list cycles and two-sum in C#

• Implement linked-list cycle detection with a HashSet and again with slow and fast pointers, then add finding where the cycle begins.
• Implement two-sum with a Dictionary and with sorted two pointers, and test duplicates, negatives and no-answer inputs.

Deliverable: Four C# solutions with tests and a stated time and space complexity for each, plus the practice exercise attempt.

Day 5 — SQL tuning and intermittent failures

• Take a slow query, read its execution plan, identify the scan or non-sargable predicate, add or fix an index, and compare plans before and after.
• Write a repeatable debugging sequence for a system that fails intermittently: confirm the symptom, find what changed, correlate logs, metrics and load, reproduce, fix and add an alert.
• Say both answers aloud, ending each with the first check you would run.

Deliverable: A before-and-after execution plan comparison and a one-page debugging checklist, each rehearsed aloud.

Day 6 — The fraud-feature case and a technical story

• Outline a new fraud-detection feature on one page: signals, data sources, decision path, handling of false positives, historical backtest, limited rollout and monitoring.
• Write the story for the question about a complex problem you solved with a technical solution: the problem, the options, why you chose one, and the result.
• Present both aloud and cut anything that does not help explain how you would know the work is effective.

Deliverable: A one-page fraud-feature outline and a written technical story, each rehearsed aloud.

Day 7 — Behavioral stories and a full run

• Write STAR stories for the challenging team dynamic, prioritizing across projects, and improving a process prompts, each with your own decision and a verifiable result.
• Say all three aloud, then replace any plural pronoun with I and cut any outcome you cannot back.
• Do a final run through one question from each type: a C# or ADO.NET answer, a coding solution, the SQL tuning answer and the fraud case.

Deliverable: Three polished STAR stories and a recorded full-run pass through one question of each type.

Behavioral stories for the collaboration round

Candidates describe the Behavioral Questions stage as an assessment of culture alignment and collaboration skills. The reported prompts ask about a challenging team dynamic, prioritizing across projects and taking initiative on a process. Since the role is described as working with product managers, designers, data analysts and cybersecurity staff, prepare stories that cross those boundaries. Use the Situation, Task, Action, Result structure and keep your own decision at the centre of every story.

1. Describe a complex problem you solved using a technical solution.
2. Tell me about a time a code review changed how you built something.
3. Tell me about a time you had to explain a technical limit to a product manager or designer.

Describe a time when you had to navigate a challenging team dynamic.

Medium · Behavioral / Leadership

Approach

1. Pick a dynamic where your own actions changed the outcome, such as two engineers disagreeing on a design or a product manager and engineering misaligned on scope. Name the roles, not a villain; a story that blames one person sinks the answer.
2. Open with the situation in two sentences, then state the task: what was at stake for delivery or the team.
3. Spend most of the answer on the actions: you spoke to each side separately to learn their goal, wrote the options and decision criteria somewhere shared, ran a small experiment or timeboxed the debate, and escalated only when needed.
4. Close with a result others could observe, such as a decision made, a blocked release unblocked, or reviews moving faster, and the relationship afterwards.
5. Use I for your steps and name teammates' contributions separately, so the listener can tell what you did.

Follow-up

• What would you do differently if you faced that dynamic again?
• How did you handle the person who still disagreed after the decision?
• How did you know the team dynamic had actually improved?

How do you prioritize tasks when working on multiple projects?

Medium · Behavioral / Leadership

Approach

1. Say your method in one or two sentences before any story: list every commitment, rank by impact and by cost of delay, check what blocks other people, and compare effort.
2. Anchor it in a real period when projects overlapped. Say what you did first, what you deferred, what you dropped or handed off, and who you told.
3. Show that you make trade-offs visible, for example by telling a product manager what slips if a new request is accepted and asking for the ranking rather than deciding silently.
4. In a financial or security-sensitive codebase, a defect that affects customers or safety usually outranks planned feature work. Say how you check that before reordering, not just that you reordered.
5. Avoid answers like I work longer hours or everything is a priority; they show no method.

Follow-up

• Two managers give you urgent but conflicting work; what do you do?
• How do you re-plan when priorities change midweek?
• Tell me about a time your prioritization turned out to be wrong.

Can you give an example of a time you took the initiative to improve a process?

Medium · Behavioral / Leadership

Approach

1. Choose an improvement with a before and after you can measure, such as a flaky release checklist, a code review template, a slow-query runbook or test automation for a manual check.
2. Show how you noticed the problem with evidence (time lost, repeated errors, review delays) rather than a feeling, and why it was not already someone's assigned job.
3. Describe how you got buy-in: you pitched it to the people affected, started with a small pilot, and adjusted after their feedback.
4. Report the result with figures you can defend, such as fewer failed releases or shorter turnaround, and whether the change stayed in use after you moved on.
5. The mistake that sinks this story is an improvement nobody adopted, or a tool built unasked that created extra work for others; address cost to teammates directly.

Follow-up

• How did you convince teammates who were sceptical?
• What did the change cost other people, and how did you handle that?
• How did you confirm the improvement was the cause of the better numbers?