FactEntry Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at FactEntry, you are at the core of our mission to deliver high-precision financial data and analytics. This role is not just about writing code; it is about building robust systems that handle complex financial datasets, including bond valuations, analytics, and large-scale web scraping operations. You will be responsible for ensuring the integrity, accuracy, and accessibility of data that our clients rely on for critical decision-making. The work at FactEntry is characterized by its technical depth and the need for high reliability. You will frequently face challenges related to data acquisition, system performance, and the maintenance of scalable infrastructure.

This guide is scoped to a Software Engineer candidate at FactEntry.

Focus areas

• Python
• Web Scraping
• Authentication / Login Handling

The FactEntry interview process

FactEntry candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

An initial assessment to evaluate the candidate's qualifications and fit for the role.

What to demonstrate

• An initial assessment to evaluate the candidate's qualifications and fit for the role
• Depth in Python

How to prepare

• Be able to walk your CV end to end in two minutes, and say why this company specifically.
• Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Technical Evaluations

Candidates undergo technical assessments, which may include live coding or online tests.

What to demonstrate

• Candidates undergo technical assessments, which may include live coding or online tests
• Depth in Python

How to prepare

• Answer aloud and timed: What strategies do you use to ensure accurate data storage and prevent duplicates in your pipelines?
• Answer aloud and timed: Could you explain the basics of website indexing and how it relates to our data acquisition goals?

Behavioral Discussions

Conversations focused on team fit and alignment with project goals.

What to demonstrate

• Conversations focused on team fit and alignment with project goals
• Depth in Python

How to prepare

• Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
• Re-read the description of the behavioral discussions above and write down what you would ask to confirm before it.

Where candidates lose points

Articulate your thought process

Even if you are unsure of the final answer, explain your reasoning. We value how you approach a problem as much as the solution itself.

Prepare for behavioral questions

Be ready to discuss your past projects in detail, specifically focusing on your role and the impact of your contributions.

Review the basics

Do not overlook the fundamentals. Many candidates are tripped up by simple questions because they only focused on advanced topics.

Questions & practice

Archive a resource graph without breaking live references or recursing

Medium · graph traversal · topological order · tenant isolation

Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.

Approach

1. Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
2. Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
3. Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
4. Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.

Follow-up

• The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
• The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?

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?

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

1. Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
2. Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
3. State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
4. Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

• The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
• Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

How would you approach a task involving SQL database management and optimization?

Medium · Technical & Domain Expertise

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. Handle the rows that do not match: that is usually the actual question.

Follow-up

• How does the query change if that join becomes one-to-many?
• What happens to this when the table is ten times larger?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

1. Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
2. Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
3. Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
4. Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

• The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
• Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

Can you describe the most significant challenges you faced in your web scraping projects, such as handling dyn

Medium · Technical & Domain Expertise

Can you describe the most significant challenges you faced in your web scraping projects, such as handling dynamic websites or authentication?

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

How do you manage IP rate limits, CAPTCHA issues, and inconsistent HTML structures when scraping data?

Medium · Technical & Domain Expertise

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. Design the error taxonomy before the success shape; callers branch on it.

Follow-up

• What happens if the caller retries after a timeout?
• How does a client discover it is on an old version of this contract?

What strategies do you use to ensure accurate data storage and prevent duplicates in your pipelines?

Medium · Technical & Domain Expertise

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

Could you explain the basics of website indexing and how it relates to our data acquisition goals?

Medium · Technical & Domain Expertise

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.

Approach

1. Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
2. Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
3. Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
4. Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.

Follow-up

• The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
• Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?

Suggested practice checklist

Built from the rounds and topics FactEntry candidates report.

Day 1 — Map the FactEntry loop

• Write out the reported sequence: Initial Screening, Technical Evaluations, Behavioral Discussions.
• For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.

Day 2 — Work Python

• Spend the session on Python, which FactEntry candidates report being tested on.
• Write one worked example in Python and time yourself on it.

Deliverable: One timed worked example in Python.

Day 3 — Work Web Scraping

• Spend the session on Web Scraping, which FactEntry candidates report being tested on.
• Write one worked example in Web Scraping and time yourself on it.

Deliverable: One timed worked example in Web Scraping.

Day 4 — Work Authentication / Login Handling

• Spend the session on Authentication / Login Handling, which FactEntry candidates report being tested on.
• Write one worked example in Authentication / Login Handling and time yourself on it.

Deliverable: One timed worked example in Authentication / Login Handling.

Day 5 — Answer out loud: Technical & Domain Expertise

• Answer aloud, timed: Can you describe the most significant challenges you faced in your web scraping projects, such as handling dynamic websites or authentication?
• Answer aloud, timed: How do you manage IP rate limits, CAPTCHA issues, and inconsistent HTML structures when scraping data?

Deliverable: Spoken answers to 2 reported Technical & Domain Expertise question(s), under time.

Day 6 — Answer out loud: Behavioral & Professional Fit

• Answer aloud, timed: Why should we hire you?
• Answer aloud, timed: Can you provide a self-introduction that highlights your professional journey and key interests?

Deliverable: Spoken answers to 2 reported Behavioral & Professional Fit question(s), under time.

Day 7 — Dry run for FactEntry

• Run one full mock under time, then write down the two questions you most want to ask your interviewers.

Deliverable: A completed timed mock and two questions to ask.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Why should we hire you?

Medium · Behavioral & Professional Fit

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Can you provide a self-introduction that highlights your professional journey and key interests?

Medium · Behavioral & Professional Fit

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Tell us about your final year project—what was your specific contribution and what challenges did you overcome

Medium · Behavioral & Professional Fit

Tell us about your final year project—what was your specific contribution and what challenges did you overcome?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Have you worked on any individual projects that demonstrate your ability to take ownership of a task from star

Medium · Behavioral & Professional Fit

Have you worked on any individual projects that demonstrate your ability to take ownership of a task from start to finish?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?