Meta·Software Engineer·Take-home Assignment·Intermediate

Intermediate

Apr 2026

Summary

Meta SWE take-home style coding round focused on real-world data wrangling in Python. The problem was messier than a typical leetcode question and felt closer to actual work, which was either refreshing or stressful depending on your tolerance for ambiguity.

Questions Asked (1)

Given two messy data sources with known issues (missing fields, corrupted records, orphaned entries, schema inconsistencies), implement a function that parses, merges, and aggregates the data into a clean summary dictionary matching a specified business output.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

The README spells out the known issues upfront which sounds helpful but also means you have no excuse for missing them.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business output and data quality issues, then outline a pipeline: parse each source with validation, merge using a defined key and conflict resolution, and aggregate with error handling. Emphasize trade-offs between correctness, performance, and simplicity, and discuss how you'd test with messy data.

Pro tip: Proactively mention that you'd log and quarantine bad records rather than silently dropping them, and that you'd make the merge logic idempotent for retries—this shows production maturity beyond just solving the algorithm.

1. Clarify requirements and data issues

Ask about the expected output schema, key fields, and how to handle missing/corrupted/orphaned data. Confirm assumptions about source formats and update frequency.

2. Design parsing and validation

Define per-source parsers that validate records against a schema, flag anomalies, and normalize fields. Decide whether to drop, fix, or quarantine bad records.

3. Define merge strategy

Choose a join key and conflict resolution rules (e.g., source priority, latest timestamp). Handle orphans and duplicates explicitly, and consider memory vs. streaming trade-offs.

4. Implement aggregation and output

Aggregate merged data into the summary dictionary, applying business rules (sums, counts, averages). Ensure the output matches the specified schema exactly.

5. Test and iterate with edge cases

Write unit tests with synthetic messy data covering missing fields, corruption, orphans, and schema mismatches. Discuss monitoring and logging for production.

Key Points to Mention

Data validation and error handling strategies (e.g., quarantine, logging, fallback defaults)
Merge key selection and conflict resolution (e.g., source priority, timestamps, business rules)
Handling orphans and duplicates (e.g., left/right/full outer join, deduplication logic)
Schema normalization and type coercion (e.g., mapping fields, handling inconsistent formats)
Aggregation correctness and performance trade-offs (e.g., streaming vs. in-memory, use of hash maps)
Testing with messy data and idempotency for retries

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