This was basically the whole interview in one question.
Start by briefly describing the business context and data sources, then walk through each layer of the pipeline (ingestion, storage, transformation, serving) explaining the design choices and trade-offs at each stage. Emphasize how the structure evolved to meet specific requirements like scalability, data quality, and maintainability, and conclude with the impact on downstream users.
Pro tip: Highlight a key trade-off you made (e.g., batch vs. streaming, normalization vs. denormalization) and why it was the right call for your use case, showing you understand that pipeline design is about balancing competing priorities.
Briefly describe the business problem, data sources, volume, velocity, and key requirements (e.g., latency, freshness, compliance) that shaped your design.
Walk through the high-level stages: ingestion, storage, processing/transformation, and serving. Mention the technologies used and why they fit the requirements.
For each stage, discuss why you chose a particular approach (e.g., batch vs. streaming, schema-on-read vs. schema-on-write) and the trade-offs involved.
Describe how you modeled the data for the analytical layer (e.g., star schema, data vault) and the measures taken to ensure data quality, lineage, and governance.
Summarize the outcomes (e.g., improved performance, reduced costs, faster insights) and reflect on what you would do differently or how the design evolved.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said star schema, justified it on query simplicity and warehouse optimizer behavior.
Start by clarifying the business context and query patterns, then recommend a star schema as the default for analytical workloads due to its simplicity and performance. Acknowledge that snowflake is appropriate when dimension tables are large and normalization is critical, but emphasize that the choice should be driven by trade-offs between query performance, storage, and maintainability.
Pro tip: Mention that in modern cloud data warehouses (e.g., Snowflake, BigQuery), storage is cheap and compute is expensive, so denormalized star schemas often win—but be ready to discuss edge cases like rapidly changing dimensions or strict data governance.
Ask about the use case: Is it for BI/reporting, real-time analytics, or ML? What are the query patterns (e.g., aggregations, drill-downs)? What are the SLAs?
Explain that star schema has denormalized dimensions for simpler queries and faster joins, while snowflake normalizes dimensions to reduce redundancy and storage, but increases join complexity.
Discuss trade-offs: star offers better query performance and usability; snowflake offers better storage efficiency and data integrity, but can hurt performance due to more joins.
Note that in cloud warehouses, columnar storage and automatic optimization often make star schema preferable; snowflake may still be useful for large, frequently updated dimensions.
State your choice based on the context, and justify it with the trade-offs. For Plaid, where data is often used for analytics and reporting, star schema is likely the better default.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with high-watermark on updated_at first, which is the obvious answer, and they immediately asked about late-arriving data.
Start by clarifying the pipeline's data sources, volume, and latency requirements, then propose a concrete incremental strategy (e.g., CDC, watermarking, or merge/upsert) with trade-offs. Explicitly enumerate failure modes like duplicates, late data, schema drift, and partial failures, and describe detection and recovery mechanisms for each.
Pro tip: Plaid deals with financial data where correctness and auditability are critical, so emphasize idempotency, exactly-once semantics, and reconciliation checks rather than just performance. Mention how you'd monitor data freshness and completeness with metrics and alerts, since silent data loss is often worse than a loud failure.
Ask about data volume, update frequency, latency SLAs, source systems, and whether the pipeline must support backfills or reprocessing. This scopes the problem and shows you avoid premature design.
Propose a method such as change data capture (CDC), timestamp/watermark-based extraction, or log-based replication, and explain why it fits the constraints. Discuss how you'd handle initial full load vs. subsequent increments.
Describe how you'd load increments into the target (e.g., merge/upsert on a primary key, partition overwrite, or append with dedup) ensuring idempotency so retries don't corrupt data. Mention use of staging tables and transactional writes.
List concrete failure modes: duplicate records, missed late-arriving data, schema evolution, partial batch failures, source outages, and clock skew. For each, explain detection (e.g., counts, checksums, watermarks) and recovery (e.g., replay, backfill, dead-letter queues).
Describe how you'd monitor freshness, volume, and data quality, and how you'd reconcile source and target counts. Explain how backfills are triggered and how they interact with ongoing incremental loads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge that out-of-order events and timezone inconsistencies can corrupt model state and lead to incorrect analytics or decisions. Then describe a robust strategy: normalize timestamps to UTC, use event-time processing with watermarks and allowed lateness, and design idempotent, order-independent updates. Finally, discuss trade-offs between correctness, latency, and complexity.
Pro tip: Emphasize that you would validate assumptions with data profiling and add monitoring for late/out-of-order events, showing you think about production reliability, not just theoretical fixes.
Explain how out-of-order events can cause incorrect aggregations, stale state, or duplicate processing, and how timezone inconsistencies can shift event times, leading to wrong windowing or joins.
Store all timestamps in UTC and include the original timezone as metadata. Convert to local time only for display or business logic that requires it.
Use event-time processing with watermarks to define completeness, allow a configurable lateness window, and buffer or reprocess late events. For stateful models, design updates to be idempotent and commutative where possible.
Ensure that reprocessing the same event multiple times does not change the model state. Use unique event IDs, deduplication, and versioned state to support replay and correction.
Track metrics like late event rate, watermark lag, and timezone conversion errors. Use these to tune allowed lateness and improve data quality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Put dedup in the staging layer, which felt right.
Start by clarifying the source of duplicates and the desired outcome (e.g., exactly-once semantics). Then explain where deduplication should live in the pipeline (e.g., ingestion, transformation, or serving layer) and how your model handles it (e.g., using primary keys, merge/upsert, or window functions). Emphasize trade-offs and alignment with business requirements.
Pro tip: Mention that deduplication should ideally happen as early as possible to reduce downstream cost, but sometimes late deduplication is necessary for auditability. Also, highlight the importance of idempotent writes to handle retries gracefully.
Ask about the definition of a duplicate (e.g., exact match or based on a key), the expected volume, latency requirements, and whether duplicates are acceptable temporarily.
Discuss options like using primary keys with upserts, window functions (e.g., ROW_NUMBER), or streaming deduplication with state stores. Consider batch vs. streaming contexts.
Evaluate placing deduplication at ingestion (e.g., in the ETL tool), in the transformation layer (e.g., SQL), or in the serving layer (e.g., materialized views). Justify based on trade-offs.
Describe how your model handles duplicates: e.g., using MERGE statements, deduplication in dbt models, or Apache Flink for streaming. Mention idempotency and exactly-once processing.
Discuss how to detect duplicates (e.g., data quality checks), handle late-arriving data, and ensure deduplication logic is maintainable and scalable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that schema evolution is inevitable and pipelines must be designed to handle it gracefully. Then walk through a layered strategy: schema registry with compatibility checks, defensive coding with schema-on-read, and automated alerting for breaking changes. Emphasize trade-offs between strict enforcement and flexibility, and how you'd validate changes in staging before production.
Pro tip: Mention that you treat schema changes as API contract changes—version them, enforce backward compatibility, and have a rollback plan. This shows you think about data as a product and understand the business impact of pipeline failures.
Use a schema registry or metadata store to capture the new schema and automatically classify the change as additive (new column), rename, type change, or deletion. This determines the risk level and required action.
Apply backward/forward compatibility rules: additive changes are usually safe, renames and type changes are breaking. Reject incompatible changes at ingestion or route them to a quarantine area for manual review.
Use schema-on-read with flexible formats (Avro, Parquet, JSON) and avoid hardcoding column names. Implement default values for missing columns and alias mapping for renames to keep downstream consumers working.
Run schema compatibility tests in CI/CD and validate data quality in staging. Use canary deployments to catch issues before full rollout, and maintain a rollback strategy.
Set up monitoring for schema drift and pipeline failures. Alert on unexpected changes and track metrics like null rates for new columns to quickly identify and resolve issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Soft deletes with an is_deleted flag and a deleted_at timestamp.
Start by clarifying the source system's delete semantics (hard vs. soft deletes) and how they propagate to your model. Then discuss the trade-offs of supporting hard deletes in your model, including data integrity, auditability, and performance. Finally, propose a strategy that balances correctness with practical constraints, such as soft deletes with periodic purging or tombstoning.
Pro tip: Emphasize that deletes are often irreversible and can break downstream consumers; propose a design that makes deletes explicit and auditable, such as using tombstone records, to avoid silent data loss.
Ask whether the source system performs hard deletes (physical removal) or soft deletes (logical flag). Understand if deletes are cascading and how they are captured in change data capture (CDC).
Determine if your model currently supports hard deletes. If not, explain how you would extend it, e.g., by adding tombstone records or a deleted_at timestamp.
Discuss the implications of hard deletes: loss of history, referential integrity issues, impact on aggregations, and potential for accidental data loss. Also consider performance and storage costs.
Recommend a balanced approach, such as soft deletes with periodic hard deletes for compliance, or using tombstones to propagate deletes while retaining auditability.
Mention handling of late-arriving deletes, idempotency, and ensuring downstream consumers are aware of delete semantics to avoid inconsistencies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge that NULL join keys are a common data quality issue and explain how your model handles them based on the join type and business requirements. Discuss the trade-offs between dropping, keeping, or flagging NULL-keyed rows, and how you ensure data integrity and transparency.
Pro tip: Proactively mention that you monitor and alert on NULL join key rates in production, as this shows you think about data reliability beyond just the immediate query.
Explain that INNER JOINs drop rows with NULL keys, while LEFT/RIGHT/FULL OUTER JOINs may keep them depending on the side. Clarify which join your model uses and why.
Discuss whether NULL keys indicate missing data, pipeline issues, or legitimate cases. Consider the impact on downstream metrics and whether dropping them could bias results.
Decide to either filter out NULL-keyed rows, keep them with a placeholder, or route them to a quarantine table. Justify your choice based on requirements and data contracts.
Add logging or alerts for NULL key occurrences and document the handling in data dictionaries or model comments to ensure transparency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.