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Microsoft·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Jun 2026

Summary

Microsoft system design round, focused entirely on ETL pipeline architecture. Pretty dense question that kept branching into new sub-topics every time I thought I'd covered enough ground.

Questions Asked (1)

Q1

Design an ETL pipeline orchestrated by a job scheduler, covering the extract, transform, and load stages, dependency management between jobs, fault tolerance, load balancing, caching, async queues, retries, and monitoring.

System DesignTechnical Trade-offsAPI & Integrations
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Suggested Approach

Start by clarifying requirements and scale, then walk through the ETL stages with a focus on orchestration, reliability, and scalability. Emphasize trade-offs and how you would monitor and operate the pipeline in production.

Pro tip: Anchor your design around idempotency and exactly-once semantics, as they are critical for fault tolerance and retries in distributed ETL systems. Also, mention how you would leverage cloud-native services (e.g., Azure Data Factory, AWS Glue) to avoid reinventing the wheel.

1. Clarify Requirements and Scale

Ask about data volume, velocity, variety, latency requirements, and SLA. Understand the source and target systems, and whether the pipeline is batch or streaming.

2. Design the ETL Stages

Detail the extract, transform, and load phases: how data is ingested (e.g., APIs, databases, files), transformed (e.g., cleansing, enrichment, aggregation), and loaded (e.g., data warehouse, data lake).

3. Orchestration and Dependency Management

Explain how a job scheduler (e.g., Airflow, Azure Data Factory) manages dependencies, triggers jobs, and handles backfills. Discuss DAGs and how to handle cross-job dependencies.

4. Fault Tolerance, Retries, and Monitoring

Describe strategies for handling failures: retries with exponential backoff, dead-letter queues, idempotent operations, and checkpointing. Outline monitoring, alerting, and logging.

5. Scalability and Performance

Discuss load balancing, caching, async queues, and partitioning to handle increasing loads. Mention trade-offs between consistency, latency, and cost.

Key Points to Mention

  • Idempotency and exactly-once processing to ensure data correctness during retries
  • Use of DAGs and dependency management in schedulers like Airflow or Azure Data Factory
  • Fault tolerance via retries, exponential backoff, dead-letter queues, and checkpointing
  • Monitoring and alerting with metrics, logs, and tracing (e.g., Prometheus, Grafana, Azure Monitor)
  • Caching strategies (e.g., Redis, CDN) and async queues (e.g., Kafka, RabbitMQ) for decoupling and load leveling
  • Load balancing and partitioning for parallel processing and scalability

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