Build a pipeline to load a daily CSV into a database

WHAT THIS TESTS: This question assesses your ability to design a basic, event-driven system using common cloud services. The interviewer wants to see if you can assemble standard components (storage, serverless compute, database) into a reliable, automated workflow. They are evaluating your understanding of triggers, IAM permissions, and basic data handling. For a senior candidate, it's also a test of pragmatism – choosing the simplest, most cost-effective solution for a simple problem.

A GOOD ANSWER COVERS: A strong answer outlines four main components in order. First, the storage layer: an S3 bucket to receive the daily CSV files. Second, the trigger mechanism: S3 Event Notifications configured to fire on s3:ObjectCreated:* events. Third, the processing logic: an AWS Lambda function that is the target for the S3 event. This function contains the code to parse the CSV, perform any minor transformations, and insert the data into the database. Fourth, the database: an Amazon RDS instance (e.g., PostgreSQL or MySQL). A senior-level answer also mentions the necessary IAM role for the Lambda function and discusses basic error handling, like using a Dead Letter Queue (DLQ) for failed invocations.

COMMON WRONG ANSWERS: A major red flag is suggesting a solution that isn't event-driven, like a cron job on an EC2 instance that polls the S3 bucket. This is inefficient and not idiomatic for the cloud. Another mistake is over-engineering with complex tools like AWS Glue or Step Functions for a single-step task. Finally, forgetting about permissions (IAM roles) or error handling (DLQs) shows a lack of production-readiness.

LIKELY FOLLOW-UPS: Expect questions that add complexity. "What if the CSV file is 10GB?" (This exceeds Lambda's limits, suggesting a switch to AWS Glue, Fargate, or streaming reads). "How would you handle schema changes?" (Discussing schema validation, versioning, or alerting). "How do you ensure you don't process the same file twice?" (Idempotency, perhaps by tracking processed file names in a DynamoDB table). "How do you monitor this pipeline?" (CloudWatch Logs for the Lambda and CloudWatch Alarms for errors).

ONE CONCRETE EXAMPLE: A daily 50MB CSV of user sign-ups is dropped into an s3://daily-signups/ bucket. An S3 event triggers a Python Lambda function. The Lambda uses the boto3 S3 client to get the object, the csv library to parse it row by row, and the psycopg2 library to connect to an RDS PostgreSQL instance. It inserts each row into a users table. The Lambda has a 30-second timeout and is configured to send failed events to an SQS queue as its DLQ. The entire process for a 50MB file should complete in under 15 seconds and cost fractions of a cent per day.