I started with the obvious layers (raw ingest, staging, curated, metrics) and that part felt fine.
Start by clarifying the business metrics and data volume, then outline a layered DAG from ingestion to curated snapshots. Emphasize how partitioning and clustering choices align with query patterns and cost efficiency.
Pro tip: Mention that you'd validate the DAG with data quality checks and monitor for late-arriving events, showing you think about production reliability, not just the happy path.
Ask about metric definitions, update frequency, data volume, and latency requirements to scope the DAG appropriately.
Outline how raw streaming events are ingested (e.g., Kafka to S3) and stored in a raw zone, partitioned by event date for efficient replay and backfill.
Describe cleaning, enrichment, and aggregation steps (e.g., sessionization, metric computation) in intermediate layers, using partitioning by date and clustering by key dimensions like user_id or subscription_id.
Explain how final snapshots are generated, partitioned by snapshot date and clustered by metric dimensions, optimized for BI queries and dashboards.
Discuss orchestration (e.g., Airflow), data quality checks, and handling of late data to ensure reliability and freshness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got uncomfortable.
Start by clarifying the grain and business rules: what defines a monthly snapshot, how cancellations and reactivations are represented, and what 'correct' means for late-arriving events. Then propose a MERGE-based upsert that recomputes the affected monthly partitions from the full event history, making the operation idempotent so reruns don't double-apply changes.
Pro tip: Emphasize idempotency and partition-level recomputation rather than incremental row-by-row updates—this shows you understand production data pipelines and avoids the classic double-apply bug on reruns.
Ask about the snapshot grain (e.g., customer-month), the event schema (event_type, event_timestamp, effective_date), and how late-arriving events should be handled. Confirm whether the target is a full monthly snapshot or a slowly changing dimension.
Describe how to derive the latest state per entity per month from the event stream, using window functions (ROW_NUMBER or LAST_VALUE) ordered by event timestamp and a tie-breaker. Explain how cancellations and reactivations are sequenced to get the correct end-of-month status.
Propose a MERGE statement that matches on the snapshot key (entity_id, month) and updates when the recomputed state differs, inserts new rows, and optionally deletes stale rows. Stress that the source is a full recomputation of affected months, not just the new events.
Explain how to identify affected months from late events (e.g., min event date per batch) and recompute only those partitions. Use a MERGE with a deterministic source query so rerunning the same batch produces the same result.
Mention validation checks (row counts, state distribution, reconciliation with source) and trade-offs between full refresh, partition recompute, and incremental merge in terms of cost, latency, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through watermarks and tracking the earliest affected partition per late-arriving batch.
Start by explaining how to identify affected months using data lineage and event timestamps, then describe a targeted recomputation process that updates only those months. Finally, outline validation using invariants such as totals, uniqueness, and referential integrity to ensure correctness.
Pro tip: Emphasize the importance of idempotency and versioning in recomputation to avoid data corruption and enable rollback. Also, mention that you would automate invariant checks as part of the pipeline to catch issues early.
Use data lineage and event timestamps to determine which months are impacted by the late data. Consider the event time and processing time to define the affected window.
Plan to recompute only the affected months by reprocessing the relevant data partitions. Ensure the process is idempotent and can handle multiple late arrivals.
Execute the recomputation using a batch or streaming job that overwrites the affected months' data. Use versioning to track changes and enable rollback if needed.
Define and check invariants such as total counts, sums, uniqueness, and referential integrity to ensure the recomputed data is correct. Compare with pre-recomputation values where applicable.
Set up monitoring and alerts for invariant violations, and automate the recomputation and validation process to handle future late data efficiently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with versioned views in a semantic layer, each view tagged with an effective date range.
Start by acknowledging that metric definitions evolve and that versioning is essential for interpretability. Propose a versioned metric registry with immutable definitions, effective dates, and clear documentation of changes. Emphasize that historical reports should reference the version active at the time of data generation, and that any restatement should be explicit and traceable.
Pro tip: Tie the versioning strategy to business impact: explain how you'd communicate definition changes to stakeholders and provide impact analysis, ensuring trust in historical trends.
Create a central repository where each metric definition is stored with a unique version ID, effective start and end dates, and a changelog. This ensures every definition is immutable and auditable.
When computing metrics, record the version used in the data pipeline and in report metadata. Historical reports should automatically display the version active at the time of their creation.
When a metric definition changes, create a new version with a future effective date. Avoid retroactively altering past versions unless a restatement is explicitly approved and documented.
Document why the definition changed, quantify the impact on historical trends, and communicate this to stakeholders. Offer tools to compare metrics across versions.
If a restatement is required (e.g., for compliance), create a new version that applies to historical periods, but clearly mark it as a restatement and preserve the original for audit.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.