Assessment of a candidates practical proficiency across the technology stack and tools relevant to their role. This includes the ability to list and explain hands on experience with programming languages, frameworks, libraries, cloud platforms, data and machine learning tooling, analytics and visualization tools, and design and prototyping software. Candidates should demonstrate depth not just familiarity by describing specific problems they solved with each tool, trade offs between alternatives, integration points, deployment and operational considerations, and examples of end to end workflows. The description covers developer and data scientist stacks such as Python and C plus plus, machine learning frameworks like TensorFlow and PyTorch, cloud providers such as Amazon Web Services, Google Cloud Platform and Microsoft Azure, as well as design tools and research tools such as Figma and Adobe Creative Suite. Interviewers may probe for evidence of hands on tasks, configuration and troubleshooting, performance or cost trade offs, versioning and collaboration practices, and how the candidate keeps skills current.
MediumTechnical
44 practiced
Your organization uses Avro with Confluent Schema Registry for streaming topics. Explain a practical process for managing schema changes safely: desired compatibility levels (BACKWARD/FORWARD/FULL), CI validation of schema changes, governance/approval workflows, and how to communicate and coordinate changes across multiple producer and consumer teams.
MediumTechnical
49 practiced
You manage a 10TB analytical dataset requiring heavy aggregations and many concurrent BI users. Compare Snowflake, BigQuery, and Redshift for this workload, covering compute/storage separation, concurrency and scaling, cost model (storage + compute + egress), query latency for interactive workloads, and operational maintenance (vacuuming, maintenance windows). Recommend one platform and justify your choice.
HardSystem Design
49 practiced
Plan a migration from an on-prem Hadoop cluster (HDFS + YARN + Hive) to cloud-native services (S3, Databricks/EMR/Dataproc, and Snowflake/BigQuery). For ~500 TB of data and many ETL jobs, describe data transfer strategies (bulk + delta), cutover plan, validation checks (checksums, row counts), security and IAM mapping, cost estimation, and rollback options.
EasyBehavioral
61 practiced
Tell me about a time in the past year when you learned a new tool (for example, Snowflake, Databricks, Terraform, or a monitoring stack) and applied it to solve a production data engineering problem. Use the STAR method: Situation, Task, Action, Result. Be specific about concrete impact: performance improvement, cost reduction, reliability increase, or time-to-insight.
MediumTechnical
48 practiced
Your team's monthly S3 bill doubled after adding daily ingests due to many small files and increased GET requests. Outline a plan to identify root causes and reduce costs: include audits, compaction strategies, lifecycle rules (Intelligent-Tiering, Glacier), compression, file sizing guidelines, and request-rate optimizations for analytics workloads.
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