Start by systematically scanning the query output for anomalies across all columns, categorizing issues by type (e.g., negative values, NULLs, duplicates, out-of-range timestamps). Then, for each issue, explain its potential impact on downstream analysis and suggest validation rules or query fixes to prevent recurrence.
Pro tip: Demonstrate a proactive mindset by not only identifying issues but also proposing automated data quality checks (e.g., dbt tests or Great Expectations) to catch these issues in the pipeline before they reach analysts.
Review the query and schema to determine what values are valid for each column (e.g., non-negative for counts, non-null for IDs, unique for primary keys, timestamps within a plausible range).
Visually inspect the output for negative numbers, NULLs in unexpected places, duplicate rows, and timestamps that are clearly out of range (e.g., future dates or before system launch).
Count how many rows are affected by each issue and group them by type to prioritize the most severe or widespread problems.
For each issue, hypothesize potential causes in the query logic (e.g., incorrect join causing duplicates, missing filter causing negative values from refunds) or upstream data sources.
Suggest immediate query corrections (e.g., adding DISTINCT, COALESCE, or WHERE clauses) and long-term solutions like data validation tests or monitoring alerts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew the fixes mechanically but stumbled a bit explaining the production monitoring angle.
Start by clearly stating the data quality issues you identified, then walk through the updated query step by step, explaining how each filter, COALESCE, deduplication, or validation check addresses a specific issue. Finally, describe how you would surface these issues in production using logging, metrics, and alerts, emphasizing proactive monitoring and root cause analysis.
Pro tip: Demonstrate a production mindset by discussing not just how to handle issues in the query but also how to prevent them upstream, such as adding data validation at ingestion and setting up automated alerts for anomalies.
Briefly recap the data quality issues you found (e.g., nulls, duplicates, invalid values) to set the context for your query updates.
Explain the specific SQL changes: use WHERE filters to exclude invalid rows, COALESCE to handle nulls, window functions or DISTINCT for deduplication, and CASE statements for validation checks.
For each modification, describe why it's necessary and how it improves data quality, ensuring the query remains performant and correct.
Describe how you would log data quality issues, emit metrics (e.g., count of nulls, duplicates), and set up alerts to notify the team when thresholds are exceeded.
Suggest upstream validation, data contracts, or automated tests to prevent similar issues from occurring in the future.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.