Zoox·Software Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Data engineering interview at Zoox centered on a single meaty data quality question over a credit card transaction schema. Pretty technical, felt more like a working session than a traditional interview.

Questions Asked (1)

Given a credit card transactions table and a vendors table, propose at least five data quality checks or validations. Cover things like integrity constraints, referential integrity, refund logic, invalid values, missing data, and anomaly detection. For at least two of your checks, write SQL that returns the failing rows.

Data ModelingRoot Cause AnalysisSystem Design

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Suggested Approach

Start by clarifying the schema and business rules, then systematically cover data quality dimensions: completeness, validity, consistency, integrity, and anomaly detection. For each check, explain the business impact and provide SQL for at least two checks that return failing rows. Conclude by discussing how to operationalize these checks in a pipeline.

Pro tip: Frame data quality checks as a tiered system: critical checks that block processing vs. warning checks that alert. This shows you understand production trade-offs and prioritization.

1. Clarify schema and business rules

Ask about the table structures, key relationships, and business rules (e.g., refunds must reference original transactions, amounts must be positive). This ensures your checks are relevant and accurate.

2. Enumerate data quality dimensions

List checks across dimensions: referential integrity, domain validity, completeness, consistency, and anomaly detection. Aim for at least five checks covering the prompt's areas.

3. Write SQL for selected checks

For at least two checks, write SQL queries that return failing rows. Use clear aliases and comments to explain the logic.

4. Explain business impact and remediation

For each check, describe the potential business impact (e.g., revenue loss, customer dissatisfaction) and suggest remediation steps (e.g., data cleansing, process fixes).

5. Discuss operationalization

Explain how to integrate these checks into a data pipeline (e.g., scheduled jobs, alerts) and how to handle failures (e.g., quarantine, manual review).

Key Points to Mention

Referential integrity: ensure every transaction's vendor_id exists in the vendors table.
Refund logic: refunds should have negative amounts and reference a valid original transaction.
Invalid values: check for negative amounts (except refunds), invalid currency codes, or future dates.
Missing data: identify NULLs in critical fields like transaction_id, amount, or vendor_id.
Anomaly detection: flag transactions with unusually high amounts or frequency compared to historical patterns.
Operationalization: implement checks as automated tests with severity levels and alerting.

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