2–5 years · Mid — SQL · Python · Spark · Airflow · Warehousing · ETL / ELT
Decides on appropriate partition keys based on query patterns and data access frequency; considers using a composite index for common query filters; evaluates the trade-offs of partitioning versus performance; plans for future growth and archiving strategies; tests performance with sample queries before full implementation.
What partitioning strategies would you consider for time-based data?
Checks the integrity of the data source against the expected schema; verifies transformation logic for anomalies; reviews logs for warnings or errors during processing; confirms the data lineage to identify where the issue may have arisen; engages with the downstream team for specific examples of discrepancies.
You discover a transformation step that misapplied business rules. How do you prevent this from happening in future iterations?
Identifies the pipeline and context; explains the specific error encountered and its immediate impact; details the investigation process and tools used; outlines steps taken to fix the issue and prevent recurrence; reflects on lessons learned and how this shaped future work.
What would you do differently if faced with a similar failure again?
Analyses the query execution plans for bottlenecks; checks for inefficient joins or missing indexes in the queries; evaluates the hardware resources and database configuration; considers caching strategies for frequently accessed data; consults with stakeholders to understand usage patterns.
You identify a query that is poorly optimized. How do you approach rewriting it to improve performance?
Names the system migrated and rationale for the migration; discusses specific technical challenges encountered; describes the strategies used to mitigate risks; details the testing process to ensure data integrity; reflects on the overall impact of the migration on the team or business.
What unexpected issues arose during the migration?
Assesses the throughput and latency requirements of the end-users; selects appropriate data streaming technologies; designs for idempotency to handle duplicate messages; ensures schema evolution compatibility between source systems; plans for dynamic scaling based on load.
You realise one of the sources is not real-time capable. How do you adapt your design to accommodate this?
Load six more — you will get questions you have not seen first.
Give me six more →Say your answer out loud before revealing. Reading the answer first feels like learning and is not.