As a Software Engineer at FIS, you are at the heart of the global financial technology ecosystem. FIS powers the movement of money and information for the world's leading financial institutions, and your code directly impacts the reliability, security, and efficiency of these critical systems. Whether you are working on Operations Software, Cloud Services, or UI/UX design, your contributions ensure that millions of transactions, user experiences, and backend processes function seamlessly at massive scale. This role requires a blend of technical precision and strategic thinking. You will not just be writing code; you will be solving complex problems that bridge legacy infrastructure with modern digital demands. You will collaborate across cross-functional teams, including product managers, cloud architects, and operations specialists, to build robust, scalable solutions. If you are driven by the challenge of maintaining mission-critical systems while driving innovation in a fast-paced environment, this role offers the perfect intersection of impact and professional growth.
Recruiter Screen
reportedInitial evaluation to assess candidate's background and fit for the role.
What to demonstrate
- Initial evaluation to assess candidate's background and fit for the role
- Depth in Software Engineering
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 Deep Dives
reportedSeries of technical interviews conducted by engineering managers and senior peers.
What to demonstrate
- Series of technical interviews conducted by engineering managers and senior peers
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: How do you ensure high availability and performance in a distributed system?
- Answer aloud and timed: Explain the trade-offs between different state management libraries in a UI/UX context.
Technical Assessment
reportedPotential coding test or practical assessment to evaluate coding skills.
What to demonstrate
- Potential coding test or practical assessment to evaluate coding skills
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: How do you approach debugging a production issue in a high-traffic environment?
- Answer aloud and timed: Walk me through the design of a scalable microservice for processing financial transactions.
System Design Interview
reportedDiscussion focused on designing scalable systems and problem-solving approaches.
What to demonstrate
- Discussion focused on designing scalable systems and problem-solving approaches
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: How would you refactor a legacy module to improve its maintainability and performance?
- Answer aloud and timed: Given a requirement for a new user interface component, how do you balance accessibility, performance, and design fidelity?
Final Evaluation
reportedFinal assessment to determine overall fit and alignment with company standards.
What to demonstrate
- Final assessment to determine overall fit and alignment with company standards
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: Describe a time you had to troubleshoot a complex performance bottleneck. What was your process?
- Answer aloud and timed: Tell me about a time you had to disagree with a technical decision. How did you handle it?
PracHub editorial advice for the preparation topics above.
Research the product
Understand which specific FIS business unit you are interviewing with. Knowing their product suite shows genuine interest.
Ask meaningful questions
At the end of your interviews, ask about the team's engineering culture, how they handle technical debt, or how they balance new feature development with production support.
Be prepared for ambiguity
In system design interviews, the prompt may be open-ended. Ask clarifying questions to define the scope before you start drawing diagrams.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
Archive a resource graph without breaking live references or recursing
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
- 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.
- 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.
- 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.
- 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
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
- 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.
- 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.
- 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.
- 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?
Explain why the owner filter ignores the listing index
The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.
Approach
- Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
- For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
- Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
- Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.
Follow-up
- 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
- A dashboard runs this for 40 owners in one page load. What changes about the design?
Find version gaps and relay lag with window functions
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?
How do you manage dependency injection in your current tech stack?
How do you manage dependency injection in your current tech stack?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- 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 ensure high availability and performance in a distributed system?
How do you ensure high availability and performance in a distributed system?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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?
Explain the trade-offs between different state management libraries in a UI/UX context.
Explain the trade-offs between different state management libraries in a UI/UX context.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Walk me through the design of a scalable microservice for processing financial transactions.
Walk me through the design of a scalable microservice for processing financial transactions.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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 refactor a legacy module to improve its maintainability and performance?
How would you refactor a legacy module to improve its maintainability and performance?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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?
Given a requirement for a new user interface component, how do you balance accessibility, performance, and des
Given a requirement for a new user interface component, how do you balance accessibility, performance, and design fidelity?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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 do you approach debugging a production issue in a high-traffic environment?
How do you approach debugging a production issue in a high-traffic environment?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Built from the rounds and topics FIS candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the FIS loop
- Write out the reported sequence: Recruiter Screen, Technical Deep Dives, Technical Assessment, System Design Interview, Final Evaluation.
- 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 5 reported rounds, with the weakest marked.
02Work Software Engineering
- Spend the session on Software Engineering, which FIS candidates report being tested on.
- Write one worked example in Software Engineering and time yourself on it.
Deliverable: One timed worked example in Software Engineering.
03Work Microsoft Cloud Services
- Spend the session on Microsoft Cloud Services, which FIS candidates report being tested on.
- Write one worked example in Microsoft Cloud Services and time yourself on it.
Deliverable: One timed worked example in Microsoft Cloud Services.
04Work UI/UX Engineering
- Spend the session on UI/UX Engineering, which FIS candidates report being tested on.
- Write one worked example in UI/UX Engineering and time yourself on it.
Deliverable: One timed worked example in UI/UX Engineering.
05Answer out loud: Technical Proficiency and Domain Knowledge
- Answer aloud, timed: How do you manage dependency injection in your current tech stack?
- Answer aloud, timed: Describe your experience with Microsoft Cloud Services and managing infrastructure as code.
Deliverable: Spoken answers to 2 reported Technical Proficiency and Domain Knowledge question(s), under time.
06Answer out loud: Problem-Solving and System Design
- Answer aloud, timed: Walk me through the design of a scalable microservice for processing financial transactions.
- Answer aloud, timed: How would you refactor a legacy module to improve its maintainability and performance?
Deliverable: Spoken answers to 2 reported Problem-Solving and System Design question(s), under time.
07Answer out loud: Behavioral and Leadership
- Answer aloud, timed: Tell me about a time you had to disagree with a technical decision. How did you handle it?
- Answer aloud, timed: How do you prioritize tasks when you have competing deadlines from multiple stakeholders?
Deliverable: Spoken answers to 2 reported Behavioral and Leadership question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Describe your experience with Microsoft Cloud Services and managing infrastructure as code.
Describe your experience with Microsoft Cloud Services and managing infrastructure as code.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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 time you had to troubleshoot a complex performance bottleneck. What was your process?
Describe a time you had to troubleshoot a complex performance bottleneck. What was your process?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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 time you had to disagree with a technical decision. How did you handle it?
Tell me about a time you had to disagree with a technical decision. How did you handle it?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
How do you prioritize tasks when you have competing deadlines from multiple stakeholders?
How do you prioritize tasks when you have competing deadlines from multiple stakeholders?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
Give an example of how you mentored a junior engineer or improved team processes.
Give an example of how you mentored a junior engineer or improved team processes.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
What do you do when you are assigned a task that falls outside your area of expertise?
What do you do when you are assigned a task that falls outside your area of expertise?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
- 01
Describe your experience with Microsoft Cloud Services and managing infrastructure as code.
- 02
Describe a time you had to troubleshoot a complex performance bottleneck. What was your process?
- 03
Tell me about a time you had to disagree with a technical decision. How did you handle it?
- 04
How do you prioritize tasks when you have competing deadlines from multiple stakeholders?
How long does the interview process typically take?
From the initial recruiter screen to a final decision, the process generally spans 3 to 6 weeks, though this can vary based on team requirements.
FIS Software Engineer candidate reports ↗What is the best way to stand out to an interviewer?
Demonstrate a "business-first" mindset. Don't just explain how your code works; explain how it solves a specific user problem or improves operational efficiency for FIS.
FIS Software Engineer candidate reports ↗Is there a coding test?
Many technical roles involve a practical assessment, either as an online challenge or a live coding session. Focus on clean, readable, and well-tested code. Do not underestimate the importance of soft skills. Even if your technical skills are perfect, failing to demonstrate teamwork or communication will hinder your candidacy.
FIS Software Engineer candidate reports ↗What topics does FIS test in interviews?
FIS interviews most often cover SQL, Linux, Stakeholder Management, Behavioral Interviewing, and Analytical Thinking. The exact emphasis depends on the specific role you apply for.
FIS Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01FIS Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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