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American Family Life Insurance- Aflac Software Engineer Interview Guide

American Family Life Insurance- Aflac Software Engineer interview guide: reported rounds, SQL, API, SSO and AWS prep, and STAR answers to rehearse.

The 60-second brief

American Family Life Insurance- Aflac is described as a supplemental insurance provider. Candidates describe the Software Engineer role as building and maintaining the systems behind claims processing, policy management and internal operations. The stack they list includes backend APIs, SQL databases, Snowflake, AWS, and integrations with enterprise platforms such as Workday and single sign-on providers. Candidates also describe a mix of older systems being modernized and newer cloud work, with postings that list Remote options alongside Columbus, GA and New York, NY.

This guide is for engineers interviewing for a Software Engineer role at American Family Life Insurance- Aflac, including platform-style roles with levels such as Platform Engineer III. It covers the three reported rounds, the SQL, API, SSO and cloud questions candidates report, the behavioral and situational questions, and a seven-day plan. It does not cover compensation or team-specific details that candidates have not reported.

Candidates report three rounds over roughly three to five weeks: an HR screening (a recruiter conversation or a recorded VidCruiter interview), a technical screening about past projects and concepts, and a final round with leadership or a cross-functional panel.

Focus areas

SQL
AWS
Snowflake

Overview

The work candidates describe is practical enterprise engineering: secure APIs that let internal systems exchange data, SQL written and tuned against large datasets in Snowflake or older relational databases, and integrations with platforms such as Workday and single sign-on providers. Requirements candidates list put SQL, relational databases, building and consuming REST APIs and an understanding of authentication such as SSO first, with AWS, Snowflake and Workday experience as extras. Prior work in insurance, healthcare or another regulated sector is described as a plus, not a requirement.

That shapes the technical preparation. Candidates describe the technical screen as only partly technical and focused on applying technology to business problems, not on algorithm puzzles. So practise explaining things you have built: why a join type was chosen, how a query was made faster, how an API authenticated callers, how a service was deployed. The reported topics (SQL, AWS, Snowflake, API design and integration, SSO) came from four reported loops, so spend your hours in that order and do not spend a week on dynamic programming.

The other half of the loop is how you present yourself. Candidates describe a traditional corporate setting where attire, punctuality, a working webcam and microphone, and a tidy background are part of the evaluation, with some reporting early rejections over small lapses. Behavioral questions cover motivation for insurance, learning quickly, accuracy, disagreeing with a manager, shifting requirements, prioritizing and explaining technical issues to non-technical people. Prepare those as structured STAR stories with a measurable result, and rehearse them out loud, on camera if possible.

The American Family Life Insurance- Aflac interview process

These are the three stages candidates describe; the exact sequence can vary by team and by whether the role is remote or based at a hub such as Columbus or New York.

HR Screening

Candidates report that this first stage is either a live conversation with a recruiter or an asynchronous VidCruiter video interview, for which they have about three days to record answers. It is described as checking your background against the role and your alignment with the company. The introductory and motivation questions candidates report (walking through your resume, why this company, what you know about the business, learning a technology quickly, keeping your work accurate) sit in this early stage. Treat a recorded interview as a real one: set up your camera and audio in advance and answer each prompt as a short, structured story.

What to demonstrate

Whether your background and resume match the role
Motivation for this company and for insurance specifically
Communication on camera or on the phone, since there is no technical deep dive reported here
Professional presentation and a working technical setup, which candidates report matter from the first contact

How to prepare

Write a present, past, future summary of your career that names SQL, API and cloud work, and rehearse it out loud
Research what supplemental insurance is and how it differs from standard health insurance, and write your reasons for choosing insurance in your own words
Test webcam, microphone, lighting and background; if you receive a VidCruiter invitation, practise recording answers to common behavioral prompts first
Have your location and working-model questions ready (remote, hybrid or on-site), since candidates report that postings vary

Technical Screening

Candidates describe a conversation with a hiring manager or senior engineer about your past projects and technical concepts, covering SQL, AWS, system design ideas and how technology is applied to business problems. They describe it as only partly technical and not an algorithm whiteboard. Reported examples include walking through a project, possibly with screen sharing, designing an API that securely pulls policyholder data from Snowflake, handling authentication and SSO when connecting an internal tool to Workday, and optimizing a slow SQL query in production. The technical question types candidates report here are inner versus left joins, secure REST API design, database performance, AWS experience and how SSO works.

What to demonstrate

Working knowledge of SQL and relational design, including joins and query tuning
Whether you can design and secure a REST API and explain authentication such as SSO
Understanding of basic cloud architecture and deployment, with AWS as the named platform
Ability to describe past projects clearly and tie technical choices to business outcomes

How to prepare

Practise the inner join versus left join explanation with a small policyholder and claims example, including unmatched rows and the WHERE-clause trap
Outline a secure REST API from scratch: authentication, authorization, input validation, TLS, rate limiting, error shape, logging, versioning
Learn the SSO redirect flow with SAML or OpenID Connect well enough to draw it, including what the identity provider sends back and how the app validates it
Prepare one project walkthrough you could present over screen share, with the architecture, your role, a problem you hit and the measured result

Final Round

Candidates describe the final stage as meetings with executive leadership or a panel of cross-functional managers, judging long-term potential and cultural fit. The situational questions candidates report belong here as a group: disagreeing with a manager about a technical approach, requirements changing at the last minute, prioritizing multiple urgent deadlines and explaining a technical issue to a non-technical stakeholder. Candidates also describe collaborating with product managers, business analysts and executives, so prepare to speak in business terms, not only technical ones.

What to demonstrate

Fit with a structured, collaborative working style, as candidates describe it
Ability to handle conflict, shifting scope and competing priorities
Communication of technical ideas to non-technical audiences
Longer-term potential and readiness for the role

How to prepare

Write four STAR stories (disagreement, changed requirements, competing deadlines, explaining a technical issue) each ending with a concrete outcome
Prepare an accuracy and reliability story you can reuse, such as a migration or report you reconciled against the source
Practise translating one technical limitation into impact, options and a recommendation in plain language
Prepare two questions about how engineers work with business analysts and product teams on this team

Where candidates lose points

Treating the video or screen-share setup casually

Candidates report early rejections over attire, a missing collared shirt or leaving the call to fetch a webcam. Dress formally, test camera, microphone, lighting and screen sharing beforehand, and join on time. For a VidCruiter invitation, record practice answers first.

Giving a generic answer to why this company and this business

Prepare a short, accurate explanation of supplemental insurance and how it differs from standard health insurance, then link it to the systems a Software Engineer works on. Answers that mention only the tech stack are what the screening is reported to filter out.

Explaining joins, SSO or API security by name only

For inner versus left join, include unmatched rows, NULLs on the right side and the trap where a WHERE filter on the right table turns a left join into an inner join. For SSO, walk the redirect, the signed assertion or token, and how the app validates it. For the REST API, name authentication, authorization, TLS, validation and logging.

Telling behavioral stories with no structure or result

Candidates are advised to use STAR. Write each story with the situation in two sentences, your own actions in the middle, and a measurable result or lesson at the end, for the disagreement, changed requirements, prioritizing and non-technical explanation questions.

Claiming AWS, Snowflake or Workday experience you cannot back up

AWS, Snowflake and Workday are described as nice-to-haves, so be exact about what you used and in what environment. For anything you have only read about, say so and describe how you would approach it.

Questions & practice

Describe a project where the requirements changed at the last minute. How did you adapt?

Medium · Situational & Problem Solving

Approach

Pick a real change with stakes: what was agreed, what changed, who asked for it, and how close to release it came. Keep the setup to two or three sentences so most of the answer covers what you did.
Show triage before coding. For example: I asked why the change was needed, sized its impact on code, data, tests and integrations, and put options to the owner (ship the original scope on the date with the change next, move the date, or drop something else). Then I got the decision confirmed in writing.
Show how you protected correctness under pressure: isolating the new behavior behind a feature flag or configuration, updating the tests to the new requirement, keeping code review, and rerunning regression checks instead of skipping them to make the date.
Close with a measurable result and the process change you made afterwards, such as earlier sign-off on business rules or a design that kept the volatile rule in one place.
Avoid the answers that sink this question: blaming the stakeholder, or a heroics story about working all night with no trade-off conversation. That shows effort but no judgment.

Follow-up

What did you cut or delay to absorb the change, and who agreed to that?
How did you confirm the late change did not break behavior that had already been tested?
What would you have done if the change had arrived the day before release?

How do you prioritize your tasks when you have multiple urgent deadlines?

Medium · Situational & Problem Solving

Approach

Give a method, then prove it with one real week. List every commitment, then rank each by impact and cost of delay against effort: who is blocked, and whether the date is hard (a release or compliance deadline) or soft.
Make the conflict visible early. For example: I told my manager and the requesters which items could not all land on time, recommended an order and got agreement, instead of quietly missing one deadline.
Sequence the work: unblock others first when that is cheap (a review, an answer, a decision), split large items so something usable ships, and renegotiate scope or dates on the rest with a specific new date.
Tell each requester when they will get their item, and track the work somewhere visible so the priorities do not live only in your head.
Avoid 'I just work longer hours' or 'everything got done' with no rule for ranking. Name what you deferred and what that cost.

Follow-up

Two senior stakeholders each say their request comes first. How do you decide, and who makes the call?
Tell me about a deadline you did miss. What happened, and what did you change afterwards?
How do you tell a request that is truly urgent from one that is only loud?

Find overlapping job attempts and peak concurrency from lease records

Medium · sweep line · intervals · leases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach

Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.

Follow-up

A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

Explain the difference between an inner join and a left join in SQL, and when you would use each.

Medium · Technical & Platform Engineering

Approach

Define both precisely: an INNER JOIN keeps only row pairs where the ON predicate is true; a LEFT JOIN keeps every row of the left table and fills the right table's columns with NULL when nothing matches. Both repeat a left row once per matching right row.
Tie each to a use: inner join when only matched pairs matter (claims together with the policy they belong to); left join when the left set must stay complete, such as every policy with its claim count, using COUNT(c.claim_id) so unmatched policies show 0 rather than the 1 that COUNT(*) returns.
Show the anti-join: LEFT JOIN claims c ON c.policy_id = p.policy_id WHERE c.policy_id IS NULL returns policies with no claims. NOT EXISTS expresses the same thing and is safe with NULLs, while NOT IN returns no rows once the subquery yields a NULL.
Name the trap: a filter on the right table in WHERE (WHERE c.status = 'OPEN') discards the NULL-extended rows and silently turns the left join into an inner join. Move that condition into the ON clause when unmatched left rows must survive.
Cover NULL keys and fan-out: a NULL join key never equals anything, so those rows drop out of an inner join and come back NULL-extended from a left join. If the right side is one-to-many, aggregate it in a subquery or CTE before joining so sums on the left table are not multiplied.

Follow-up

How would you list policies that have no claims, and why can NOT IN give a different answer from NOT EXISTS?
After you add a WHERE condition, your left join returns fewer rows than the left table has. What happened, and how do you fix it?
When would you use a FULL OUTER JOIN instead, for example when reconciling records from two systems?

Find version gaps and relay lag with window functions

Medium · window functions · event tables · frame semantics

outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.

Approach

Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.

Follow-up

Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?

Walk me through how you would design a secure REST API from scratch.

Medium · Technical & Platform Engineering

Approach

Start with resources and contracts: noun-based URLs (/policies/{id}/claims) and method semantics (GET is safe, PUT and DELETE are idempotent, POST is not, so a creating POST takes an Idempotency-Key header). Add an OpenAPI schema and a status-code plan that separates 401 (not authenticated) from 403 (authenticated but not allowed), plus 404, 409 and 429.
Authenticate every call, over TLS only. Users come in with OAuth 2.0 / OpenID Connect bearer tokens from an identity provider; services use the client-credentials grant or mutual TLS. Validate each JWT's signature against the issuer's keys and check iss, aud and exp. Keep secrets in a secrets manager, never in code or query strings.
Authorize at the object level on every request and deny by default. Checking that this caller may read policy 123, and not just that the token is valid, closes broken object level authorization, the first item in the OWASP API Security Top 10. Use scopes or roles for coarse access and ownership checks for individual rows.
Treat all input as hostile: validate bodies and parameters against the schema, cap payload and page sizes, and reach the database only through bind parameters so input can never become SQL. On output, return only the fields the caller needs, and keep personal data out of URLs, logs and error bodies (no stack traces).
Add the operational layer: rate limiting at a gateway that returns 429 with Retry-After, an audit log of who read or changed which record, versioning (/v1 or a header) so the contract can evolve without breaking clients, and key rotation. Close with the trade-off: short-lived tokens limit exposure but require refresh handling.

Follow-up

A JWT stays valid until it expires. How do you cut off a user or client whose access was just revoked?
How would a batch job or partner system call this API when no user is present?
How would you test that one customer cannot read another customer's records?

Explain how Single Sign-On (SSO) works under the hood.

Medium · Technical & Platform Engineering

Approach

Name the parties and the trust set up in advance: the identity provider (IdP) that logs users in, the application (service provider or relying party) that trusts it, and the browser that carries messages between them. In SAML, trust comes from exchanged metadata: the IdP signing certificate, entity IDs and the app's Assertion Consumer Service URL. In OIDC it comes from a registered client_id, exact redirect URIs and the IdP's JWKS keys.
Walk the SAML SP-initiated flow. The app finds no session and redirects the browser to the IdP with an AuthnRequest. The IdP authenticates the user or reuses its existing session, which is what makes it single sign-on. The browser then POSTs a signed assertion to the ACS URL. The app checks the signature, audience, recipient, NotBefore/NotOnOrAfter, InResponseTo and the assertion ID (against replay), then creates its own session.
Walk OIDC as the modern alternative: the authorization code flow with state, nonce and PKCE. The IdP redirects back with a short-lived code, and the app exchanges it at the token endpoint, over a back channel, for an ID token (a signed JWT) and an access token. The app validates the signature via JWKS plus iss, aud, exp and nonce. The ID token says who the user is; the access token is for calling APIs.
Explain why the second app needs no password: the IdP keeps its own session cookie on its own domain, so the next redirect returns an assertion or code without a login prompt. Each app still holds a separate local session, which is why logout and deprovisioning are harder than login (SAML Single Logout, OIDC back-channel logout, SCIM for removing accounts).
List the security pitfalls: unvalidated state (login CSRF), loose redirect URI matching (code or token leakage), accepting unsigned or wrongly scoped assertions, XML signature wrapping in hand-rolled SAML parsing, and clock skew on validity windows. Use a maintained library rather than parsing assertions yourself.

Follow-up

When would you choose SAML over OpenID Connect, or the reverse, for a new internal tool?
A user is disabled in the identity provider but still has an open session in your app. What happens, and how would you close that gap?
How does the app know an assertion was not forged or replayed?

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

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.
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.
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.
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

The seven days follow the reported loop: the business and recruiter conversation first, then SQL, API and SSO, cloud and database performance, and finally the behavioral and situational stories for the later rounds, ending with a full rehearsal.

Day 1 — Map the loop and the business

Write the three reported rounds (HR Screening, Technical Screening, Final Round) and mark which questions each type of conversation is likely to cover, using the reported groupings only.
Read the company's public pages on what it sells and write a half-page on supplemental insurance versus standard health insurance in your own words.
Write your answer to why you want to work here and to what you know about the business, noting any gap you must verify.

Deliverable: A one-page loop map plus a half-page business summary and a written answer to the why-this-company question.

Day 2 — SQL joins and database performance

Create small policyholders and claims tables and write inner join, left join and anti-join queries, including a case where a WHERE filter on the right table silently removes unmatched rows.
Take a slow query on a table you can access, read its EXPLAIN plan, change one index or predicate, and record before and after timings.

Deliverable: A saved join cheat sheet with outputs, one before and after query plan, and a solved SQL practice question.

Day 3 — Secure REST API and SSO

Outline a REST API from scratch for policy data: endpoints, authentication, authorization, input validation, rate limiting, error shape, versioning and audit logging.
Draw the single sign-on flow for SAML and for OpenID Connect, marking what the identity provider returns and how the application validates it.
Sketch how an API could pull policyholder data from Snowflake without exposing more than the caller needs, covering service credentials and column-level access.

Deliverable: One API design page and two annotated SSO sequence diagrams, plus a short Snowflake access note.

Day 4 — AWS, Snowflake and production troubleshooting

Write a one-page walkthrough of a deployment you have done on AWS: build, deploy target, IAM, secrets, monitoring and rollback.
List Snowflake basics you can explain: warehouses, micro-partitions, clustering, query history and the Query Profile.

Deliverable: A written AWS deployment story, a Snowflake fundamentals list and one finished debugging practice question.

Day 5 — Screening-round stories

Write and rehearse your present, past, future introduction and a story about learning a technology quickly.
Write your accuracy answer using named checks (tests, review, reconciliation) and one error you caught.
Record all three on a webcam in business attire and review framing, pace and eye line.

Deliverable: Three recorded answers and a list of fixes for each.

Day 6 — Situational stories for the final round

Write STAR stories for the manager disagreement, the last-minute requirement change and the multiple urgent deadlines, each with a measurable result.
Write the non-technical explanation story using impact, cause, options and recommendation.
Practise each aloud, then cut any story that has no visible decision of your own.

Deliverable: Four STAR stories on one page, each ending in a result and a lesson.

Day 7 — Full rehearsal and setup check

Run a full mock: introduction, why this company, an inner versus left join explanation, a secure API walkthrough and one disagreement story, spoken in sequence.
Do a complete equipment check: camera, microphone, lighting, background, screen sharing and formal attire.
Prepare two questions for each of the technical and final conversations.

Deliverable: A recorded mock run, a setup checklist ticked off and a written list of questions to ask.

Behavioural preparation

Candidates describe the behavioral questions as testing motivation for insurance and this company, adaptability when requirements change, handling disagreement and explaining technical limits to business people. Candidates are advised to answer in STAR form with a clear business result.