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SentiLink Software Engineer Interview Guide

SentiLink Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

A Software Engineer at SentiLink plays a critical role in building and scaling the core infrastructure that power identity verification and real-time fraud prevention. In this position, you will design, implement, and optimize backend systems that process millions of transactions to detect complex fraud vectors, such as synthetic identity theft and account takeover. Because SentiLink operates at the intersection of financial technology, security, and machine learning, your work directly impacts the financial safety of top-tier banks, credit unions, and fintech platforms. As a member of the engineering team, you will tackle highly complex challenges involving high-throughput data pipelines, low-latency APIs, and advanced clustering algorithms.

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

Focus areas

  • Interview process communication
  • System Design
  • Backend / Platform engineering

The SentiLink interview process

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

Recruiter Screen

Initial conversation to align on background, compensation expectations, and interest in the company.

What to demonstrate

  • Initial conversation to align on background, compensation expectations, and interest in the company
  • Depth in Interview process communication

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 Screening

Includes a hiring manager interview and a technical assessment, which may be a take-home assignment or live coding session.

What to demonstrate

  • Includes a hiring manager interview and a technical assessment, which may be a take-home assignment or live coding session
  • Depth in Interview process communication

How to prepare

  • Answer aloud and timed: Implement a parser for nested financial transaction payloads, ensuring robust error handling for malformed inputs.
  • Answer aloud and timed: Optimize a lookup system that matches incoming identity data against a massive blacklist of known fraudulent attributes.

Virtual Onsite Panel

Comprehensive evaluation of engineering capabilities, product intuition, and behavioral fit.

What to demonstrate

  • Comprehensive evaluation of engineering capabilities, product intuition, and behavioral fit
  • Depth in Interview process communication

How to prepare

  • Answer aloud and timed: Design a high-throughput, low-latency API gateway that handles real-time identity verification requests from multiple external partners.
  • Answer aloud and timed: How would you architect a distributed clustering system to group millions of identity records based on evolving fraud markers?

Where candidates lose points

Leverage your recruiter

The recruiters at SentiLink are exceptionally supportive. Take advantage of any prep calls or advice they offer before your technical and leadership rounds; they want you to succeed and can provide invaluable context on what specific interviewers look for.

Before your final rounds with the Head of Engineering and the CEO, ask your recruiter for a quick briefing

They can provide excellent guidance on how to frame your background and align your answers with the founders' current priorities.

Showcase your interest in fraud

You do not need to be a fraud expert, but you must be curious about the domain. Read up on synthetic identity fraud and think about how bad actors might try to bypass identity verification systems. Showing that you think like a fraud investigator will make you stand out.

Questions & practice

Given a stream of transaction data, identify anomalous patterns or duplicate entries within a rolling time win

Medium · Coding & Algorithmic Problem Solving

Given a stream of transaction data, identify anomalous patterns or duplicate entries within a rolling time window.

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Implement a parser for nested financial transaction payloads, ensuring robust error handling for malformed inp

Medium · Coding & Algorithmic Problem Solving

Implement a parser for nested financial transaction payloads, ensuring robust error handling for malformed inputs.

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Optimize a lookup system that matches incoming identity data against a massive blacklist of known fraudulent a

Medium · Coding & Algorithmic Problem Solving

Optimize a lookup system that matches incoming identity data against a massive blacklist of known fraudulent attributes.

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

  1. One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
  2. Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
  3. State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
  4. Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

  • A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
  • Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

  1. Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
  2. Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
  3. Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
  4. Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

  • The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
  • A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

Design an efficient algorithm to group or cluster related user profiles based on shared, potentially fraudulen

Medium · Coding & Algorithmic Problem Solving

Design an efficient algorithm to group or cluster related user profiles based on shared, potentially fraudulent identifiers.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

Design a high-throughput, low-latency API gateway that handles real-time identity verification requests from m

Medium · System Design & Architecture

Design a high-throughput, low-latency API gateway that handles real-time identity verification requests from multiple external partners.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

How would you architect a distributed clustering system to group millions of identity records based on evolvin

Medium · System Design & Architecture

How would you architect a distributed clustering system to group millions of identity records based on evolving fraud markers?

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

Design a rate-limiting and throttling mechanism to protect internal fraud-detection microservices from sudden

Medium · System Design & Architecture

Design a rate-limiting and throttling mechanism to protect internal fraud-detection microservices from sudden traffic spikes.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

How would you structure a data pipeline that ingests, processes, and stores transaction logs for asynchronous

Medium · System Design & Architecture

How would you structure a data pipeline that ingests, processes, and stores transaction logs for asynchronous fraud analysis without impacting live API performance?

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?

Read a technical API specification and write a clear, professional email explaining to a partner integration e

Medium · Domain, Product & Fraud FIT

Read a technical API specification and write a clear, professional email explaining to a partner integration engineer why their API request failed and how to fix it.

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?

Walk us through how you would balance system security (low false-positive rates) with user friction in an iden

Medium · Domain, Product & Fraud FIT

Walk us through how you would balance system security (low false-positive rates) with user friction in an identity verification flow.

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?

Why are you interested in the fraud prevention space, and what specific fraud vectors do you find most challen

Medium · Domain, Product & Fraud FIT

Why are you interested in the fraud prevention space, and what specific fraud vectors do you find most challenging to solve programmatically?

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 would you design a dashboard feature that helps risk analysts easily visualize and investigate clustered n

Medium · Domain, Product & Fraud FIT

How would you design a dashboard feature that helps risk analysts easily visualize and investigate clustered networks of fraudulent accounts?

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

  1. Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
  2. Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
  3. Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
  4. Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

  • The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
  • How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the rounds and topics SentiLink candidates report.

Day 1 — Map the SentiLink loop

  • Write out the reported sequence: Recruiter Screen, Technical Screening, Virtual Onsite Panel.
  • 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 Interview process communication

  • Spend the session on Interview process communication, which SentiLink candidates report being tested on.
  • Write one worked example in Interview process communication and time yourself on it.

Deliverable: One timed worked example in Interview process communication.

Day 3 — Work System Design

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

Deliverable: One timed worked example in System Design.

Day 4 — Work Backend / Platform engineering

  • Spend the session on Backend / Platform engineering, which SentiLink candidates report being tested on.
  • Write one worked example in Backend / Platform engineering and time yourself on it.

Deliverable: One timed worked example in Backend / Platform engineering.

Day 5 — Answer out loud: Coding & Algorithmic Problem Solving

  • Answer aloud, timed: Design an efficient algorithm to group or cluster related user profiles based on shared, potentially fraudulent identifiers.
  • Answer aloud, timed: Given a stream of transaction data, identify anomalous patterns or duplicate entries within a rolling time window.

Deliverable: Spoken answers to 2 reported Coding & Algorithmic Problem Solving question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

  • Answer aloud, timed: Design a high-throughput, low-latency API gateway that handles real-time identity verification requests from multiple external partners.
  • Answer aloud, timed: How would you architect a distributed clustering system to group millions of identity records based on evolving fraud markers?

Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.

Day 7 — Answer out loud: Domain, Product & Fraud FIT

  • Answer aloud, timed: Read a technical API specification and write a clear, professional email explaining to a partner integration engineer why their API request failed and how to fix it.
  • Answer aloud, timed: Walk us through how you would balance system security (low false-positive rates) with user friction in an identity verification flow.

Deliverable: Spoken answers to 2 reported Domain, Product & Fraud FIT question(s), under time.

Behavioural preparation

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

Describe a time when you had to make a technical trade-off between shipping a feature quickly versus ensuring

Medium · Behavioral & Leadership

Describe a time when you had to make a technical trade-off between shipping a feature quickly versus ensuring long-term system scalability.

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 me about a highly ambiguous technical problem you faced. How did you define the requirements and drive th

Medium · Behavioral & Leadership

Tell me about a highly ambiguous technical problem you faced. How did you define the requirements and drive the project to completion?

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?

Describe a situation where you had a strong disagreement with a product manager or peer regarding a system's d

Medium · Behavioral & Leadership

Describe a situation where you had a strong disagreement with a product manager or peer regarding a system's design. How did you resolve it?

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?

Walk us through your career progression. What motivated your transitions, and what are you looking to achieve

Medium · Behavioral & Leadership

Walk us through your career progression. What motivated your transitions, and what are you looking to achieve in your next role?

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?