Meta·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Meta data engineer technical screen done in CoderPad's Python project mode. You get a README with two messy data sources and a business question, and you have to write a function that joins them, handles every documented data quality issue, and returns a summary dict. More like a mini take-home crammed into a live session.

Questions Asked (1)

Given two data sources from different upstream systems with known schema inconsistencies, missing records, and orphaned foreign keys, implement a function that joins and processes them to return a summary dictionary answering the stated business question.

Data ModelingTechnical Trade-offsSystem Design

Author's notes

The README lays out the known issues which sounds helpful until you realize you have to handle all of them and comment on each decision.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the business question and the expected output schema, then explicitly state your assumptions about data quality and join semantics. Walk through a layered approach: ingest and normalize each source, reconcile schema inconsistencies, handle missing records and orphaned keys, then join and aggregate into the summary dictionary. Emphasize trade-offs between correctness, performance, and simplicity, and mention how you would validate the result.

Pro tip: Proactively call out data quality issues and propose a data quality report alongside the summary—this shows you think about production readiness and helps stakeholders trust the output. Also, mention that you would log or flag orphaned records rather than silently dropping them, which is often a key differentiator in interviews.

1. Clarify requirements and assumptions

Ask about the exact business question, expected output format, and tolerance for missing data. State assumptions about join keys, schema mappings, and what constitutes a valid record.

2. Normalize and reconcile schemas

Map fields from both sources to a common schema, handling naming differences, type mismatches, and missing columns. Decide on default values or null handling for missing fields.

3. Handle data quality issues

Identify and decide how to treat missing records and orphaned foreign keys—whether to drop, impute, or flag them. Consider using outer joins to preserve data and detect anomalies.

4. Join and aggregate

Perform the join using the appropriate keys, then aggregate the data to answer the business question. Choose between inner, left, or full outer join based on the desired semantics.

5. Validate and summarize

Check for unexpected row counts, nulls, or duplicates. Produce the summary dictionary and optionally include a data quality report with counts of dropped or orphaned records.

Key Points to Mention

Schema reconciliation: mapping fields, handling type mismatches, and dealing with missing columns.
Join semantics: choosing between inner, left, right, and full outer joins based on business needs and data quality.
Handling missing records: strategies like dropping, imputing, or flagging missing data.
Orphaned foreign keys: detecting and deciding whether to exclude, include with nulls, or report them.
Performance considerations: indexing, partitioning, or using efficient data structures for large datasets.
Validation and testing: unit tests with edge cases, data quality checks, and logging for observability.

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