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Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Interviewed for a BA role at Google and got hit with a data integration question that felt more like an engineering problem than anything I'd prepped for.

Questions Asked (1)

Q1

How do you handle joining data from different sources when the IDs don't match up consistently?

Data ModelingTechnical Trade-offsRoot Cause Analysis
Author's notes

Wasn't expecting something this technical in a BA screen.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data sources and the nature of the ID mismatches, then describe a systematic process for profiling, matching, and validating joins. Emphasize trade-offs between accuracy, performance, and complexity, and highlight the importance of root cause analysis to prevent future mismatches.

Pro tip: Mention that you would first check if the mismatches are due to data quality issues or schema differences, and propose a short-term fix (e.g., fuzzy matching) while advocating for a long-term solution like a canonical ID system.

1. Understand the Data and Mismatches

Profile the data sources to identify the types and causes of ID mismatches (e.g., formatting, missing values, different granularity).

2. Choose a Matching Strategy

Select an appropriate technique such as exact matching, fuzzy matching, or probabilistic record linkage based on the mismatch patterns and business requirements.

3. Implement and Validate

Implement the join logic, possibly using a multi-step process, and validate the results with metrics like match rate and false positive rate.

4. Handle Edge Cases and Scale

Address edge cases (e.g., many-to-many relationships) and ensure the solution scales with data volume, considering performance optimizations.

5. Root Cause and Prevent

Analyze root causes of mismatches and propose long-term fixes such as standardizing ID formats or creating a master data management system.

Key Points to Mention

  • Data profiling and understanding the source of mismatches
  • Trade-offs between different matching techniques (exact, fuzzy, probabilistic)
  • Use of auxiliary data or heuristics to improve matching accuracy
  • Validation metrics and iterative refinement
  • Scalability and performance considerations
  • Root cause analysis and preventive measures

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