2–5 years · Mid — SQL · Python · Spark · Airflow · Warehousing · ETL / ELT
Analyzes the transformation logic to find where data might be dropped; checks source data for completeness; implements logging to track record counts through each pipeline stage; creates a temporary table to isolate missing records; reviews upstream processes for changes that might affect data completeness.
After identifying the source of the missing data, what steps do you take to implement a fix?
Checks the job logs for error messages; identifies the stage at which the timeout occurs; reviews data volume and processing time for previous runs; inspects resource allocation and concurrency settings; considers whether data skew is affecting performance.
The timeout seems related to a specific transformation. What transformations would you look at first?
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?
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?
Sets up a robust error-handling mechanism to log the error and alert the team; implements a fallback procedure to skip erroneous records while continuing processing; reviews the data validation rules to catch format issues early; schedules a review session to discuss the data source integrity; considers implementing a dead-letter queue for persistent errors.
After implementing skipping logic, you find out that the skipped records contain critical information. How do you address this?
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?
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