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Meta·Software Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Meta data engineer technical screen on CoderPad, SQL only. They dropped a broken query in front of me and told me it was AI-generated, which I thought was a funny way to frame it. The whole thing was about finding and fixing the bugs while talking through my reasoning.

Questions Asked (1)

Q1

You're given a SQL query with at least four intentional errors covering syntax mistakes, wrong join types, redundant joins, fan-out issues, and missing or hallucinated columns. Find and fix all of them, explaining your reasoning for each change as you go.

Root Cause AnalysisTechnical Trade-offsData Modeling
Author's notes

Running it first before reading it carefully was probably the wrong call.

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AI HintsAI Generated

Suggested Approach

Start by reading the query aloud and breaking it into logical clauses (SELECT, FROM, JOIN, WHERE, GROUP BY). Validate each clause against the schema and expected result set, fixing errors in a logical order (syntax first, then join logic, then column references). Explain each fix in terms of correctness, performance, and data integrity.

Pro tip: Before diving into fixes, ask clarifying questions about the schema and expected output—this shows you think about requirements first. Also, mention that you'd run the query after each fix to isolate errors, demonstrating a systematic debugging approach.

1. Parse and Validate Syntax

Check for basic syntax errors like missing commas, misspelled keywords, or incorrect clause order. Fix these first to ensure the query can execute.

2. Verify Schema and Column References

Cross-check all column names against the actual schema to catch hallucinated or missing columns. Ensure all referenced columns exist and are spelled correctly.

3. Analyze Join Logic and Redundancy

Examine each JOIN to ensure the correct type (INNER, LEFT, etc.) is used based on the desired result set. Remove redundant joins that don't affect the output or cause duplication.

4. Check for Fan-Out and Aggregation Issues

Look for joins that multiply rows unintentionally (fan-out) and ensure GROUP BY or DISTINCT is used appropriately. Verify that aggregate functions are applied correctly.

5. Explain Reasoning and Trade-offs

For each fix, articulate why it's necessary and any performance or correctness trade-offs. Discuss alternative approaches if applicable.

Key Points to Mention

  • Syntax errors: missing commas, misspelled keywords, incorrect clause order
  • Wrong join types: using INNER instead of LEFT when preserving unmatched rows is needed
  • Redundant joins: joining the same table multiple times unnecessarily or joins that don't affect the result
  • Fan-out issues: one-to-many joins causing row multiplication, requiring DISTINCT or aggregation
  • Missing or hallucinated columns: referencing columns not in the schema or misspelling column names
  • Performance implications: how fixing joins and removing redundancy can improve query efficiency

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