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Senior Engineer - Data Ingestion
Posted Sep 17, 2026 · We last checked this listing on Oct 6, 2026
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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 found a data quality issue that nobody had noticed yet. How did you catch it and what did you build so it wouldn't happen again?
What they are checking: proactive ownership versus reactive bug-fixing mindset
Describe a situation where a trader or researcher asked you for something that, technically, was a bad idea. How did you handle that conversation?
What they are checking: ability to push back constructively on non-technical stakeholders without damaging the relationship
Tell me about a time you disagreed with another engineer on the data team about how to solve a problem. What happened?
What they are checking: collaboration style and how they resolve technical conflict within a team
What would you do
Say a trader flags that a key metric on their dashboard looks wrong right before market open. How do you figure out if it's a source data issue, a transformation bug, or something downstream, and what do you do in the moment versus after?
What they are checking: triage instincts under time pressure and whether they separate firefighting from root-cause work
How would you approach migrating an existing pipeline that traders currently depend on daily onto a new platform, without disrupting their workflow?
What they are checking: risk management and communication skills during infrastructure change
How do you think about SLAs for data pipelines that feed both junior traders checking dashboards and executives making bigger calls? Does the answer change based on who's using it?
What they are checking: whether they can prioritize competing service-level demands across a broad user base
If you had to lower compute and storage costs across the firm's data pipelines without hurting reliability, where would you start looking?
What they are checking: practical optimization thinking and understanding of cost drivers in large pipelines
About the work
Tell me about a data pipeline you built or owned that handled hundreds of terabytes or more. What did the architecture look like, and where did it break under load?
What they are checking: whether the candidate has real hands-on scale experience versus theoretical knowledge
Walk me through how you would design a pipeline to ingest tick-level market data and make it queryable by both traders and quants with very different latency needs.
What they are checking: ability to balance conflicting stakeholder requirements in a concrete system design
What's your experience with open table formats like Iceberg or Delta, and query engines like Trino or Spark? Tell me about a specific decision you made using one of these tools.
What they are checking: depth versus buzzword familiarity with modern data platform tooling
Write or talk through a SQL query that would detect a sudden shift in data distribution for a source feed, something that might signal an upstream data quality problem.
What they are checking: raw SQL fluency and practical data quality instincts
Tell me about the most complex system you've designed from scratch. What tradeoffs did you make between cost, reliability, and speed?
What they are checking: software architecture judgment and cost-awareness at scale
Have you worked with cloud infrastructure or HPC workloads for data processing? Walk me through a specific project where you had to optimize for either cost or performance.
What they are checking: concrete cloud/HPC experience rather than resume keyword matching
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Practice this out loudThe full job description
As published by Chicago Trading Company.