📊 Product Management

Feature A correlates with retention. Should we invest more?

Tests your ability to move beyond clichés to propose concrete analysis. A great answer questions the correlation, suggests cohort analysis or A/B testing, and probes for confounding variables. Red flag: just saying 'correlation isn't causation' with no plan.

How would you design a product management dashboard?

Tests your ability to structure data into a decision-making narrative. A good answer moves from a high-level summary (DAU) to trends (retention) and then actionable details (feature adoption). A red flag is simply listing charts without a narrative connection.

Bar Chart vs. Line Chart for Market Share Comparison?

Tests basic chart selection: comparing static categories vs. showing trends. A bar chart is correct for comparing discrete companies at one point in time. A line chart wrongly implies a time-series relationship. Red flag: choosing a line or pie chart.

When is a pie chart an appropriate choice for visualization?

Tests data viz principles for part-to-whole data. A good answer defines this use case, gives a clear example (market share), and lists pitfalls like too many slices or similar values. A red flag is defending them for complex data or time-series analysis.

How do you manage an event schema change?

This tests your understanding of data contracts and backward compatibility. A great answer details a phased, backward-compatible rollout (add nullable field), starting with consumers (warehouse) then producers, and mentions using a schema registry for…

Challenges of Grouping by a High-Cardinality Dimension

This tests your grasp of how data shape impacts system resources. A great answer explains that high cardinality explodes memory usage for aggregation state, increases CPU load, and hurts storage compression.

Trade-offs: Pre-aggregation vs. querying raw event data

This tests your grasp of data engineering trade-offs for analytics dashboards. A great answer contrasts pre-aggregation (fast, cheap, stale) with on-the-fly queries (slow, costly, fresh) and mentions hybrid solutions.

How do you ensure accurate counts with duplicate analytics events?

Tests your grasp of data integrity under at-least-once delivery. Explain why COUNT(*) is inflated, then propose deduplication using a unique event ID. Mention trade-offs of stateful processing. A red flag is ignoring the cost or the need for a unique ID.

Explain a star schema and its advantages for analytics

Tests your grasp of data modeling trade-offs for OLAP vs. OLTP. A good answer defines fact/dimension tables, then explains how denormalization and fewer joins improve query speed for analytics. A red flag is confusing it with a snowflake schema.

How would you diagnose a slow dashboard query?

This tests your systematic approach to performance tuning. A great answer investigates query optimization, strategic indexing/partitioning, and the data model (like star schema).

Why use p50/p95 over mean for API response times?

Tests your grasp of statistical distributions for UX metrics. A good answer explains how outliers skew the mean, while percentiles (p50, p95) better represent typical and worst-case user experiences.

Design a Scalable Data Governance Framework

This tests your grasp of decentralized data governance (Data Mesh). A great answer outlines four principles: domain ownership, data as a product, a self-serve platform, and federated computational governance.

How would you debug a critical data quality issue in a pipeline?

Tests your systematic debugging of a distributed system under pressure. A great answer contains the impact, traces data lineage backward from the report to the source, and then proposes preventative tooling.

How do you create a 'golden record' for customers?

Tests your grasp of data governance and systems thinking. A strong answer defines master data, outlines a phased approach (discovery, rule-setting, implementation), and covers ongoing stewardship.

How to handle schema evolution in a CDC analytics pipeline?

This tests your ability to design resilient CDC pipelines. A strong answer outlines automated schema detection, using a flexible format like Avro, and enforcing governance with a schema registry. A red flag is proposing manual fixes for every change.

Explain data lineage and how you'd implement it

Tests your practical data governance knowledge. Define lineage (origin, transforms, movement), then outline a solution: metadata collection from services, a central store (graph DB), and a visualization tool (Amundsen/OpenLineage).

What is a data schema and why enforce it on ingestion?

This tests your grasp of data governance. Define a schema as a data blueprint. Explain that enforcement on ingestion prevents "garbage in, garbage out" by validating types and formats, ensuring data is usable for analytics.

How would you validate user-submitted email addresses at ingestion?

Tests your understanding of data validation beyond simple regex, focusing on robustness and system-level thinking. A good answer covers format checks, DNS/MX record validation, and blocking disposable services.

How would you handle 10% nulls in a key column?

This tests your ability to diagnose data issues before solving. First, investigate why data is null. Then, discuss trade-offs of dropping vs. imputing with the mean or median, considering the impact on the dashboard's accuracy.