Data Engineer, Cumberland/Options
Posted Sep 30, 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 worked closely with traders or researchers to build a dataset they needed.
What they are checking: Ability to understand user needs and turn vague requests into useful data products.
Describe the most important lesson you've learned from a data pipeline failure.
What they are checking: Learning from incidents and building that lesson into future designs.
What would you do
Crypto venues trade around the clock. How does that change how you build and operate data systems?
What they are checking: Awareness that there are no quiet hours for maintenance, which affects deployments, backfills and on-call.
A trader says yesterday's data has gaps. How do you investigate and backfill without breaking downstream users?
What they are checking: Careful handling of corrections in shared data that many systems and people depend on.
How do you handle schema changes from an upstream exchange feed without warning?
What they are checking: Defensive design and fast response when external sources change unexpectedly.
About the work
The team builds datasets and data infrastructure for crypto and options trading. How would you design a pipeline that ingests market data from many exchanges reliably?
What they are checking: Practical pipeline design covering ingestion, normalization, retries and monitoring for high-volume market data.
How do you guarantee data quality and integrity at large volumes?
What they are checking: Concrete techniques like validation, idempotent loads and reconciliation rather than vague promises of quality.
Walk me through how you'd design an API that gives researchers fast access to historical options data.
What they are checking: Thinking about query patterns, storage layout and performance from the user's point of view.
What storage formats and systems have you used for time series data, and what tradeoffs did you hit?
What they are checking: Real experience with columnar formats, partitioning and databases suited to large time series.
How would you normalize options data from venues that use different symbology and contract specs?
What they are checking: Understanding of reference data challenges specific to options and crypto derivatives.
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As published by DRW.