Structured Problem Solving and Frameworks Questions

Assessment of a candidate's ability to apply repeatable, logical frameworks to break ambiguous problems into manageable components, identify root causes, weigh options, and recommend a defensible solution with an implementation plan. Topics include defining the problem and success criteria, gathering context and constraints, decomposing the problem using mutually exclusive collectively exhaustive thinking, generating alternatives, evaluating trade offs by impact and effort, and sequencing execution. Interviewers will look for clear narration of the thinking process, use of data and evidence, awareness of assumptions, and the ability to adapt a framework to different domains such as product, operations, or analytics. This canonical topic also covers systematic analysis techniques, methodological rigor, and presentation of conclusions so others can follow and act on them.

MediumTechnical

0 practiced

You need to build a decision framework for when to denormalize data for analytics versus keeping normalized source-of-truth schemas. List the criteria and thresholds you would use (e.g., query latency, join cardinality, update frequency) and give two concrete examples where denormalization is recommended and two where it is not.

MediumTechnical

0 practiced

As a data engineer, how would you structure a postmortem process for major data incidents? Describe the steps from detection to closure, template fields you would require (e.g., timeline, root cause, mitigations), how to measure remediation effectiveness, and how to ensure learnings are shared across teams.

MediumTechnical

0 practiced

Design a framework to evaluate whether to build an internal tool for dataset discovery versus adopting an off-the-shelf data catalog. Include criteria for build vs buy (customization, integration cost, maintenance), an estimated timeline, and a pilot approach to reduce risk.

MediumTechnical

0 practiced

Describe a structured approach to quantify and prioritize automation opportunities across repeating data engineering tasks (e.g., dataset onboarding, schema validation, backfill runs). Include how you'd measure ROI, estimate implementation effort, and decide which automations to deliver first.

HardSystem Design

0 practiced

You must choose between two alternative architectures to handle a 10x growth in event traffic next quarter: (A) scale existing batch jobs horizontally on larger clusters, (B) move to micro-batch/streaming ingestion and processing. Construct a decision framework: list the evaluation criteria, measurement approach for each criterion, and a recommendation outline with risks.

Unlock Full Question Bank

Get access to hundreds of Structured Problem Solving and Frameworks interview questions and detailed answers.

Join thousands of developers preparing for their dream job.