A Software Engineer at Zelis Healthcare plays a critical role in transforming the healthcare financial ecosystem. By designing, developing, and optimizing high-throughput transaction engines, payment gateways, and claim-routing platforms, you directly impact how payers, providers, and consumers interact. The work here sits at the high-stakes intersection of fintech and healthcare IT, requiring systems that are not only highly performant but also compliant with strict healthcare regulations like HIPAA. Your contributions will directly influence the efficiency of medical billing and payments across the United States. Whether you are optimizing SQL databases, scaling backend services in.NET/C#, or managing enterprise data warehouses with Snowflake, your engineering decisions will reduce administrative friction and lower healthcare costs. This is an environment where code quality, system resilience, and data security are paramount, making it an exceptionally rewarding space for engineers who thrive on solving complex, real-world problems.
Recruiter Screen
reportedInitial screening call with a recruiter to assess candidate qualifications and fit.
What to demonstrate
- Initial screening call with a recruiter to assess candidate qualifications and fit
- Depth in DSA (Data Structures & Algorithms)
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 Discussion
reportedIn-depth technical conversation with the hiring manager to evaluate technical skills.
What to demonstrate
- In-depth technical conversation with the hiring manager to evaluate technical skills
- Depth in DSA (Data Structures & Algorithms)
How to prepare
- Answer aloud and timed: Describe how dependency injection works in.NET and how it improves code testability.
- Answer aloud and timed: What are the differences between abstract classes and interfaces, and how do you use them to enforce design patterns?
Deep-Dive Interview
reportedComprehensive interview with system architects focusing on technical depth and problem-solving.
What to demonstrate
- Comprehensive interview with system architects focusing on technical depth and problem-solving
- Depth in DSA (Data Structures & Algorithms)
How to prepare
- Answer aloud and timed: Walk through a real-time scenario where you would use polymorphism to handle different types of healthcare claim formats.
- Answer aloud and timed: How do you optimize a slow-running SQL query that joins multiple large tables containing patient claims?
PracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
Review your resume thoroughly: Interviewers at Zelis Healthcare frequently ask detailed questions about the projects, technologies, and architectures listed on your resume. Be ready to explain the "why" behind your technical decisions in previous roles.
Going into the loop without having done this.
Emphasize real-world testing: When asked to write code, always discuss how you would test it. Demonstrating a proactive approach to unit testing with realistic scenarios rather than dummy data will set you apart from other candidates.
Going into the loop without having done this.
Some candidates have reported communication gaps or delays after completing final rounds. Stay proactive—if you do not receive feedback within a week of your interview, send a polite follow-up email to your recruiter to request an update.
Going into the loop without having done this.
Showcase domain interest: While prior healthcare experience is not always required, demonstrating an interest in healthcare fintech, payment integrity, and data security will show interviewers that you are aligned with the company's mission.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
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?
Identify the heaviest tenants in a five-minute window under memory pressure
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
- 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.
- 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.
- 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.
- 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?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
How do you optimize a slow-running SQL query that joins multiple large tables containing patient claims?
How do you optimize a slow-running SQL query that joins multiple large tables containing patient claims?
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- 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?
Stop tag and share joins from fanning out a page
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
- 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.
- 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.
- 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.
- 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?
Explain the difference between interface inheritance and class inheritance in C#, and provide a scenario where
Explain the difference between interface inheritance and class inheritance in C#, and provide a scenario where you would choose one over the other.
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 implement encapsulation to protect sensitive payment data within a transaction processing service?
How do you implement encapsulation to protect sensitive payment data within a transaction processing service?
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?
Describe how dependency injection works in.NET and how it improves code testability.
Describe how dependency injection works in.NET and how it improves code testability.
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?
What are the differences between abstract classes and interfaces, and how do you use them to enforce design pa
What are the differences between abstract classes and interfaces, and how do you use them to enforce design patterns?
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 through a real-time scenario where you would use polymorphism to handle different types of healthcare cla
Walk through a real-time scenario where you would use polymorphism to handle different types of healthcare claim formats.
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?
Explain the concept of indexing and detail the trade-offs between clustered and non-clustered indexes.
Explain the concept of indexing and detail the trade-offs between clustered and non-clustered indexes.
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 would you design a database schema to support a multi-tenant healthcare payment application?
How would you design a database schema to support a multi-tenant healthcare payment application?
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?
What strategies do you use to ensure data integrity and transactional consistency across distributed database
What strategies do you use to ensure data integrity and transactional consistency across distributed database systems?
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?
Read latency spikes on a sixty-second sawtooth
The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.
Approach
- Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
- Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
- Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
- De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.
Follow-up
- The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
- How does your answer change if the recompute takes 4 seconds instead of 250 ms?
Built from the rounds and topics Zelis Healthcare candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Zelis Healthcare loop
- Write out the reported sequence: Recruiter Screen, Technical Discussion, Deep-Dive Interview.
- 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.
02Work DSA (Data Structures & Algorithms)
- Spend the session on DSA (Data Structures & Algorithms), which Zelis Healthcare candidates report being tested on.
- Write one worked example in DSA (Data Structures & Algorithms) and time yourself on it.
Deliverable: One timed worked example in DSA (Data Structures & Algorithms).
03Work OOP (Object-Oriented Programming)
- Spend the session on OOP (Object-Oriented Programming), which Zelis Healthcare candidates report being tested on.
- Write one worked example in OOP (Object-Oriented Programming) and time yourself on it.
Deliverable: One timed worked example in OOP (Object-Oriented Programming).
04Work SQL
- Spend the session on SQL, which Zelis Healthcare candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
05Answer out loud: C# and Object-Oriented Programming (OOP)
- Answer aloud, timed: Explain the difference between interface inheritance and class inheritance in C#, and provide a scenario where you would choose one over the other.
- Answer aloud, timed: How do you implement encapsulation to protect sensitive payment data within a transaction processing service?
Deliverable: Spoken answers to 2 reported C# and Object-Oriented Programming (OOP) question(s), under time.
06Answer out loud: Database, SQL, and Data Warehousing
- Answer aloud, timed: How do you optimize a slow-running SQL query that joins multiple large tables containing patient claims?
- Answer aloud, timed: Explain the concept of indexing and detail the trade-offs between clustered and non-clustered indexes.
Deliverable: Spoken answers to 2 reported Database, SQL, and Data Warehousing question(s), under time.
07Answer out loud: Behavioral & Team Collaboration
- Answer aloud, timed: Describe a time when you had to work with a difficult stakeholder or team member. How did you resolve the conflict?
- Answer aloud, timed: Tell me about a project where the requirements changed mid-way through development. How did you adapt?
Deliverable: Spoken answers to 2 reported Behavioral & Team Collaboration 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 cloud data platforms like Snowflake and how you approach data storage and admini
Describe your experience with cloud data platforms like Snowflake and how you approach data storage and administration at scale.
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 when you had to work with a difficult stakeholder or team member. How did you resolve the conf
Describe a time when you had to work with a difficult stakeholder or team member. How did you resolve the conflict?
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 project where the requirements changed mid-way through development. How did you adapt?
Tell me about a project where the requirements changed mid-way through development. How did you adapt?
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?
Walk me through a situation where you identified a performance bottleneck in production and took the initiativ
Walk me through a situation where you identified a performance bottleneck in production and took the initiative to fix 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 your tasks when managing multiple tight deadlines?
How do you prioritize your tasks when managing multiple tight deadlines?
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 when you mentored a junior engineer or helped a teammate overcome a technical roadblock.
Describe a time when you mentored a junior engineer or helped a teammate overcome a technical roadblock.
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 cloud data platforms like Snowflake and how you approach data storage and administration at scale.
- 02
Describe a time when you had to work with a difficult stakeholder or team member. How did you resolve the conflict?
- 03
Tell me about a project where the requirements changed mid-way through development. How did you adapt?
- 04
Walk me through a situation where you identified a performance bottleneck in production and took the initiative to fix it.
How difficult is the Software Engineer interview at Zelis Healthcare?
The difficulty is generally rated as average to difficult. The technical rounds are highly practical, focusing heavily on C# fundamentals, real-time coding scenarios, and database optimization rather than abstract, complex dynamic programming algorithms.
Zelis Healthcare Software Engineer candidate reports ↗What is the typical timeline from the initial application to an offer?
The timeline can vary. While some candidates report a very fast turnaround of less than two weeks, others experience a slower process, particularly when coordination between global teams and local recruiters is required. On average, expect the process to take three to four weeks.
Zelis Healthcare Software Engineer candidate reports ↗Does Zelis Healthcare offer remote or hybrid work options?
Yes, Zelis Healthcare offers hybrid and remote work arrangements depending on the specific role, team, and location. Be sure to discuss your preferences with the recruiter during your initial screening call.
Zelis Healthcare Software Engineer candidate reports ↗How should I prepare for the database-related questions?
Focus on relational database design, query optimization, indexing strategies, and writing complex SQL joins. If you are interviewing for a data-centric role, make sure you are also familiar with modern data warehousing concepts, specifically platforms like Snowflake.
Zelis Healthcare Software Engineer candidate reports ↗How hard is the Zelis Healthcare interview?
Candidates most commonly rate Zelis Healthcare interviews as medium, based on 85 reported interviews. About 42% of candidates who interview go on to receive an offer.
Zelis Healthcare Software Engineer candidate reports ↗What topics does Zelis Healthcare test in interviews?
Zelis Healthcare interviews most often cover Stakeholder Management, Requirements Gathering, Process Improvement, Communication Skills, and Data-Driven Decision Making. The exact emphasis depends on the specific role you apply for.
Zelis Healthcare Software Engineer candidate reports ↗Where is Zelis Healthcare headquartered?
Zelis Healthcare is headquartered in Boston, MA.
Zelis Healthcare Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Zelis Healthcare Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22
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