Zoox·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Data engineering interview at Zoox focused on SQL-based data quality checks for transactional data. One open-ended question but it had a lot of layers to it, felt more like a mini case study than a typical SQL round.

Questions Asked (1)

What data quality checks would you add to a transactions and vendors table? Walk through potential edge cases or anomalies, propose five checks, and write SQL for two of them.

Data ModelingRoot Cause AnalysisProduct Analytics & Metrics

Author's notes

The 'five checks' constraint was actually useful because it forced me to think beyond the obvious.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and business context of the transactions and vendors tables, then systematically categorize data quality dimensions (completeness, uniqueness, validity, consistency, timeliness). Propose five concrete checks that map to these dimensions, and for two of them, write SQL that is efficient and handles edge cases like NULLs and duplicates.

Pro tip: Tie each check to a real-world failure mode (e.g., duplicate vendor payments, orphaned transactions) and mention how you'd monitor and alert on these checks in production, showing you think beyond just writing queries.

1. Clarify schema and business context

Ask about the table structures, primary/foreign keys, and what the data represents (e.g., transaction types, vendor relationships). This ensures your checks are relevant and you understand potential anomalies.

2. Identify data quality dimensions

Map the problem to standard dimensions: completeness, uniqueness, validity, consistency, and timeliness. This provides a structured way to brainstorm checks.

3. Propose five specific checks

For each dimension, suggest a concrete check, such as null checks, duplicate detection, referential integrity, value range validation, and cross-field consistency. Explain what each check catches.

4. Write SQL for two checks

Choose two checks that are most critical or illustrative (e.g., duplicate transactions and orphaned vendor IDs) and write clear, efficient SQL. Use CTEs or window functions if needed, and handle NULLs appropriately.

5. Discuss edge cases and monitoring

Mention edge cases like NULLs in join keys, timezone issues, or late-arriving data. Explain how you'd integrate these checks into a pipeline with alerting and dashboards.

Key Points to Mention

Completeness: Check for NULLs in critical columns like transaction_id, vendor_id, amount, and date.
Uniqueness: Detect duplicate transactions (e.g., same transaction_id or same vendor_id + amount + date).
Referential integrity: Ensure every vendor_id in transactions exists in the vendors table (no orphans).
Validity: Validate that amounts are positive, dates are within a reasonable range, and vendor statuses are from an allowed set.
Consistency: Cross-check that transaction totals match sum of line items, or that vendor contact info is consistent across records.
Timeliness: Monitor for stale data or delays in transaction ingestion (e.g., transactions older than X days not processed).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.