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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.
Design a data quality framework for a modern data platform.
Tests your ability to design a systematic data quality strategy. A great answer outlines a framework starting with governance (roles), then profiling/assessment, defining standards, and finally implementing pipeline controls.
How do you debug out-of-memory errors in a Spark job?
Tests your systematic debugging of distributed systems. A great answer diagnoses via the Spark UI, investigates data skew and inefficient code (e.g., shuffles), and only then tunes memory or partition configs. Red flag: immediately increasing executor memory.
Compare a data warehouse, data lake, and lakehouse
Tests your grasp of data architectures for BI vs. ML workloads. Contrast warehouses (structured) and lakes (raw), then explain how a lakehouse adds ACID/schema features to a lake's storage. A red flag is confusing schema-on-write vs. schema-on-read.
What is data partitioning in a cloud data warehouse?
Tests your grasp of performance and cost trade-offs in data systems. A good answer defines partitioning, explains partition pruning for performance, and links it directly to reduced data scan costs.
How do you handle late-arriving data in a streaming pipeline?
Tests understanding of event time vs. processing time and the mechanisms (watermarks, triggers, allowed lateness) to ensure correctness. Answer by distinguishing time types, using event-time windowing, defining watermarks, and configuring allowed lateness.
Describe star and snowflake schemas and their trade-offs.
Tests your grasp of data warehouse design trade-offs. Define star (denormalized dimensions) and snowflake (normalized dimensions) schemas. Contrast them: star is faster for queries but uses more space; snowflake is space-efficient but requires more joins.
What is a data warehouse vs. a transactional database?
Tests your grasp of read-optimized (OLAP) vs. write-optimized (OLTP) systems. A great answer defines warehouses for analysis, contrasts them with transactional DBs for operations, and explains the resulting differences in workload, schema, and data structure.
Build a pipeline to load CSVs into a database
Tests your grasp of event-driven architecture and basic ETL. A good answer outlines a trigger (storage event), a processing function (serverless), and a destination (database), mentioning error handling. A red flag is describing a manual or cron-based process.
ETL vs. ELT: Key differences and when to use each?
This tests your understanding of modern data architecture trade-offs. A good answer explains the T vs. L order, then links ETL to structured, legacy systems and ELT to flexible, cloud-native data lakes/warehouses.
Guarantee at-least-once delivery for a critical event?
This tests your grasp of atomicity without 2PC. A great answer outlines the Transactional Outbox pattern: write the event to a DB table in the same transaction as the business logic, then use a relay process. A red flag is relying on simple try/catch blocks.
Design a client-side event batching system for a high-traffic app
This tests your grasp of frontend performance and data reliability. Outline a batching strategy (timer/size), then explain using visibilitychange with navigator.sendBeacon() to prevent data loss on unload. A red flag is suggesting synchronous XHR.
How do you approach user identity stitching across devices?
This tests your grasp of event-driven data pipelines and identity management. A strong answer outlines an identifier hierarchy, the "stitch" event (login), and a backend process for retroactively applying a canonical user ID.
Build vs. Buy: Third-Party vs. In-House Analytics
Tests your grasp of the time-vs-control trade-off. A great answer weighs speed vs. customization and total cost of ownership. Advocating for 'build' without considering the massive, ongoing maintenance cost is a major red flag.
How would you debug a sudden drop in a key metric?
Tests your systematic debugging of complex data systems. First, validate the drop against a source of truth and segment the data. Then, hypothesize causes like misattribution or data loss.