Outline an automated pipeline to load a daily CSV into a database

WHAT THIS TESTS: Even though the prompt sounds beginner, at a senior level the interviewer is evaluating whether you think about operability, failure modes, and data quality rather than just listing services. They want to see if you understand the difference between a demo script and a production pipeline.

A GOOD ANSWER COVERS: First, an event-driven trigger such as an object-created notification from the storage bucket rather than a blind cron poll, because polling introduces latency and unnecessary cost. Second, a lightweight validation step before loading, which means checking file format, expected headers, row count bounds, and maybe checksums to catch upstream corruption early. Third, an idempotent loading strategy, meaning the pipeline can safely rerun without creating duplicates; common patterns include loading into a staging table and performing a merge or upsert, or using a partition swap for daily snapshots. Fourth, basic observability and alerting, specifically logging each stage, tracking file processing state, and paging when a file fails validation or the database load errors out. Fifth, a brief mention of security and cost, such as using IAM roles instead of long-lived credentials and choosing serverless components to avoid paying for idle compute.

COMMON WRONG ANSWERS: The biggest red flag is proposing a manual process or a simple cron job on a VM with no retry logic. Another weak answer jumps straight to heavy orchestration tools like Airflow for a single daily file, showing poor cost judgment. Candidates also stumble by ignoring schema evolution, suggesting a rigid load that breaks when a new column appears, or by failing to address what happens when the same file lands twice.

LIKELY FOLLOW-UPS: The interviewer might ask how you would handle late-arriving files or backfills for historical dates. They could also probe schema drift, asking how you would detect and adapt when the CSV format changes. Another common tangent is cost optimization, comparing serverless ETL against a persistent compute instance for this volume.

ONE CONCRETE EXAMPLE: On AWS, you could use an S3 event notification to trigger a Lambda function that validates a CSV of a few hundred megabytes and writes it to an S3 staging prefix. A Glue job or another Lambda then loads the data into a PostgreSQL-compatible Aurora instance using a staging table. After the load succeeds, a merge statement upserts records into the final table based on a natural key or date partition, and CloudWatch alarms fire if the Lambda errors or the row count drops below ninety percent of the expected threshold.