A decision with incomplete data

What the interviewer evaluates

This question tests bias for action and practical risk mitigation. In data engineering, waiting for perfect specifications or fully clean schemas from upstream teams can stall projects for weeks. The interviewer wants to see whether you can make a calculated, reversible choice rather than freezing in analysis paralysis.

Structuring your response

• Situation: Describe a project with missing upstream specifications, unknown data distributions, or unconfirmed consumer requirements.
• Task: Clarify what had to be built and why waiting carried more business cost than moving forward with explicit assumptions.
• Action: Detail how you bounded the risk, such as choosing a reversible two-way door decision, putting logic behind a configuration flag, staging raw data, and writing an idempotent backfill script.
• Result: Share the measurable outcome, including how quickly you unblocked downstream consumers and what you would do differently in hindsight.

Sample response

A sample answer might sound like this: Our team needed to ingest transaction events from a new payment gateway to support daily financial reconciliations. Launch was two weeks away, but the vendor API documentation lacked clear nullability rules and enum values for dispute states. Halting the ingestion build would have delayed our reporting launch by a month. My task was to design the ingestion schema so downstream financial models could begin integration testing without waiting on slow vendor support tickets. Instead of guessing rigid column types, I staged the raw API response directly into a JSON variant column in our bronze layer. In silver, I parsed only the confirmed primary identifiers and monetary amounts, while storing unconfirmed dispute metadata inside a flexible key-value map. I isolated this parsing step behind an Airflow configuration flag and wrote an automated backfill script that could replay the raw JSON bronze table if new status codes appeared. This choice unblocked two analytics engineers, allowing them to build reconciliation marts ten days ahead of launch. When the vendor finally delivered the complete data dictionary, only four custom fields needed updates, which our replay script backfilled across 1.2 million rows in twenty minutes. In hindsight, I would have scheduled a direct call with their technical solutions architect earlier to confirm critical enum values instead of waiting on email support.

Pitfalls to avoid

• Waiting indefinitely for perfect requirements instead of taking calculated, reversible risks
• Choosing rigid architectures that cannot be rolled back or replayed when assumptions change
• Failing to mention a concrete mitigation plan like idempotent reloads or staging raw data
• Blaming external teams for the missing context instead of owning the path forward