Software Engineer - Data Engineering
Posted Jul 13, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a time you disagreed with a teammate or stakeholder about how a pipeline should be built. What happened.
What they are checking: how they handle technical disagreement and whether they can be convinced by evidence
Describe a situation where you had to mentor a junior engineer through a problem rather than just fixing it yourself.
What they are checking: genuine mentoring instinct versus doing the work for someone to save time
Tell me about a time you inherited a system with a tech stack you thought was wrong for the job. What did you do about it.
What they are checking: whether they push for improvement constructively or just complain, matches the 'challenge the status quo' language in the posting
What would you do
You're asked to onboard a brand new data source that the quant team wants for backtesting, but they need it structured differently than how it arrives. How do you approach designing that pipeline.
What they are checking: ability to gather requirements from a non-engineering stakeholder and design schema/transformations accordingly
A batch job that normally finishes by 6am is still running at 9am and Trading is asking where their data is. Walk me through what you'd do in the moment.
What they are checking: composure and triage skill under real-time pressure with business stakeholders watching
About the work
Walk me through a streaming pipeline you've built end to end - what was the data source, how did it flow through, and where did it land.
What they are checking: real hands-on experience with streaming architecture versus theoretical knowledge
Say you're using Kafka to feed a Spark job that writes into Delta Lake, and downstream consumers start seeing duplicate records. How would you track down where the duplication is happening.
What they are checking: debugging methodology across a multi-hop pipeline and understanding of exactly-once versus at-least-once semantics
How would you design a data quality SLA for a dataset that trading strategies depend on intraday - what would you actually measure and how would you alert on breaches.
What they are checking: whether they can translate a vague requirement like 'data quality' into concrete, monitorable metrics
Tell me about a time you had to choose between Java/Scala and Python for a data engineering task. What drove the decision.
What they are checking: depth of the JVM background versus surface familiarity, and judgment about tool fit
What's your experience running workloads on Kubernetes or EKS - what kinds of problems have you had to solve there, versus just deploying containers.
What they are checking: depth of operational experience with the orchestration layer, not just familiarity with the term
Tell me about the largest scale data problem you've worked on - how big was the data, and what broke first as it grew.
What they are checking: actual experience with 'big data' scale versus inflated resume language
How do you decide what belongs in a batch pipeline versus a streaming one, when either could technically work.
What they are checking: architectural judgment and cost/complexity tradeoff thinking
What does your testing strategy look like for a data pipeline - what do you actually write tests for, and what do you rely on monitoring to catch instead.
What they are checking: understanding of the difference between correctness testing and production observability
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Practice this out loudThe full job description
As published by Akuna Capital.