Senior Data Engineer

Walk me through a time you designed a data pipeline from scratch. What tradeoffs did you make between speed of delivery and long-term scalability?

What they are checking: whether they can balance immediate desk needs against durable architecture, which the posting explicitly calls out

Describe a situation where you had to explain a technical data problem to someone without a technical background, like a trader or a desk head.

What they are checking: communication skill across the technical/business divide, which the posting stresses heavily

Tell me about a project where you were given very little direction on architecture and had to make the major decisions yourself.

What they are checking: the posting wants someone who needs minimal guidance, so they're checking for real autonomy versus dependence on senior review

Describe a time a pipeline you built broke in production during market hours. What did you do, and what changed afterward?

What they are checking: incident response instincts and whether failures lead to systemic fixes, given the low tolerance for downtime in trading

Say a trader tells you a number on their dashboard looks wrong right before market open. How do you figure out whether it's a data quality issue, a pipeline lag, or the trader misreading the data?

What they are checking: how they triage under time pressure with a non-technical stakeholder who needs an answer fast

If two trading desks both want the same shared dataset restructured in incompatible ways to fit their models, how do you handle that?

What they are checking: stakeholder negotiation and architectural thinking when demands conflict

What's a time you had to push back on a request from the desk because you thought it would hurt data reliability or long-term architecture, and how did that conversation go?

What they are checking: whether they can hold a technical line diplomatically against business pressure without just saying yes to everything

How would you design a data warehouse that needs to handle petabytes of high-fidelity market data while still letting analysts run ad hoc queries quickly?

What they are checking: depth of real experience with warehouse architecture at scale versus textbook knowledge

Tell me about a time you built automated data quality checks. What kinds of failures were you trying to catch, and how did you decide what threshold triggered an alert versus what could wait?

What they are checking: practical judgment on where to draw the line between noisy alerting and missed real problems

What's your approach to choosing between a batch ETL process and a streaming pipeline for a given dataset, and can you give an example where you made that call?

What they are checking: whether they reason from data characteristics and latency needs rather than defaulting to one pattern

How do you think about schema design and versioning when the underlying trading data model keeps changing as new instruments or strategies get added?

What they are checking: experience with evolving production systems rather than static, greenfield designs

How would you approach building an analytic visualization layer for traders who need to make split-second decisions, versus one for quants doing longer research?

What they are checking: understanding that different users need different tradeoffs between speed, depth, and simplicity