FAANG Specific Technology and Culture Questions
Understanding of what makes each FAANG company's technical challenges and culture unique. Google focuses on scale and distributed systems. Amazon emphasizes customer obsession and operational excellence. Meta focuses on mobile and infrastructure. Apple emphasizes hardware-software integration and user experience. Netflix is known for microservices and freedom and responsibility culture. Microsoft has become increasingly cloud-focused with Azure. Understanding each company's technical philosophy helps you source engineers who align with that culture.
EasyTechnical
0 practiced
You're onboarding as a Data Engineer on Microsoft's Azure team. Describe a cloud-native data pipeline end-to-end using Azure services (for example Event Hubs, Data Factory, Azure Databricks, Synapse, Data Lake Storage). Explain why you'd pick each component, and discuss scalability, security, and cost considerations for a production deployment serving analytics and ML workloads.
MediumSystem Design
0 practiced
Design an event ingestion pipeline that handles 10 million daily mobile events with peaks of 500k events/min, similar to what Meta might operate. Outline client SDK buffering and upload strategies, ingestion endpoint design, queuing, validation, streaming vs batch processing options, storage layout, and how you'd guarantee idempotence and low client battery/network usage while supporting downstream joins and analytics.
MediumTechnical
0 practiced
You're operating petabyte-scale nightly ETL on Google Cloud and need to reduce cost growth. Describe strategies to optimize storage and compute costs: partitioning, clustering, choice of table formats (e.g., Avro/Parquet/ORC), lifecycle policies, using preemptible/spot instances, query optimization, and pre-aggregation. Provide examples of expected cost impacts and trade-offs with query latency or complexity.
HardTechnical
0 practiced
You're leading a transformation from a centralized data team to product-aligned data engineering squads (Netflix/Meta model). Draft a 12-month transformation plan with milestones, KPIs (SLAs, velocity, data quality), training programs, governance changes, pilot rollout approach, and risk mitigation for duplicated work and knowledge loss.
HardTechnical
0 practiced
Design instrumentation and analysis strategies for running A/B experiments on a new Apple OS feature where devices update infrequently and telemetry can be delayed significantly. Address how to capture exposure, handle offline exposure logging, ensure statistical power, mitigate bias from opt-in devices, and robustly attribute metrics to variations despite delayed telemetry.
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