senior-data-engineer

Establish data quality in ETL pipelines by implementing data quality contracts and lineage tracking across warehouses, lakes, and marts. This approach enforces quality standards while providing governance and monitoring throughout the data pipeline lifecycle.

Orchestrate batch and streaming ETL workflows by applying end-to-end pipeline orchestration patterns tailored for modern data stacks. This provides actionable guidance for architecture decisions, data modeling, and DataOps best practices across your data infrastructure.

Schema evolution and lineage tracking work by operationalizing governance patterns that monitor and manage changing schemas across warehouses and lakes. This ensures data quality enforcement and maintains consistent lineage throughout the evolving data pipeline infrastructure.

Yes, you can use this approach for both data warehouse and data lake architectures. It designs and operationalizes scalable data pipelines and governance across modern data stacks, supporting batch and streaming ETL, data modeling, and schema evolution for both environments.

You need DataOps governance and data quality contracts when establishing scalable data pipelines that require enforced quality standards and lineage tracking. This becomes necessary when managing schema evolution, data modeling patterns, and orchestration across complex modern data stacks.