domains / families / data / senior-data-engineer
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues. World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.
The two copies overlap on core data engineering topics (ETL/ELT, streaming, data quality, orchestration) but each contains material steps and constraints the other lacks. The benchflow copy is a more complete, self-contained procedure with specific workflow steps, expected outputs, and inline script invocations. The LeoYeAI copy is a structured catalog that adds a detailed Architecture Decision Framework (Batch vs Streaming, Lambda vs Kappa, Warehouse vs Lakehouse) and trigger phrases, but defers its workflows to external reference files. Crowning one would hide the other's unique decision-making framework or its concrete implementation steps.