Commure·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Commure had me do a coding screen that was basically a data pipeline problem disguised as a CSV parsing exercise. More design-heavy than I expected for what felt like a mid-level screen.

Questions Asked (1)

You're given three CSV files representing providers, patients, and appointments. Parse them line by line, validate each row, and join them into a single output where each appointment record embeds the relevant patient and provider info. Invalid rows or references to nonexistent IDs should be logged and skipped without crashing.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

The join logic itself wasn't hard but I got tripped up on the structure they wanted.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline a streaming, line-by-line parsing strategy with validation and error handling. Describe how you would build in-memory indexes for patients and providers to enable efficient joins, and explain how to handle invalid rows and missing references gracefully. Finally, discuss trade-offs around memory, performance, and data integrity.

Pro tip: Emphasize idempotency and observability: log skipped rows with enough context (file, line number, reason) so failures can be audited and reprocessed without crashing the pipeline.

1. Clarify requirements and constraints

Ask about file sizes, expected data volume, schema details, and whether real-time or batch processing is needed. Confirm error-handling expectations (e.g., log and skip vs. fail fast).

2. Design parsing and validation

Read each CSV line by line using a streaming parser to handle large files. Validate row structure, field types, and required fields; collect errors with line numbers and reasons.

3. Build lookup indexes for joins

Load patients and providers into hash maps keyed by ID for O(1) lookups. Consider memory trade-offs and whether to use disk-based or external sorting if data is too large.

4. Perform join and handle missing references

For each valid appointment, look up patient and provider IDs; if either is missing, log the appointment as invalid and skip it. Embed the found records into the output.

5. Output and error reporting

Write the joined records to the output CSV. Aggregate and report skipped rows with details (file, line, reason) for auditing and potential reprocessing.

Key Points to Mention

Streaming line-by-line parsing to handle large files without loading everything into memory
Validation of each row: schema, data types, required fields, and referential integrity
Use of hash maps for O(1) lookups when joining appointments with patients and providers
Graceful error handling: log invalid rows with context and continue processing
Trade-offs between memory usage and performance, especially for large datasets
Idempotency and observability: ensure the process can be rerun safely and errors are traceable

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