senior-data-engineer

Design scalable data pipelines by orchestrating ETL/ELT workflows with Airflow and dbt, applying production-grade patterns for data quality, monitoring, and lineage. This approach handles complex end-to-end data workflows across multi-source environments.

Enforce data quality and governance by implementing data contracts, validation checks, and DataOps practices. This ensures reliability and compliance across your data infrastructure through codified monitoring and lineage patterns.

Choose between batch and streaming or Lakehouse and Warehouse architectures using provided decision guidance. It offers practical examples and architecture patterns like SCD and Data Vault to guide modern data stack implementations.

Yes, you can use Python, SQL, and Spark together for ETL workflows. The design supports multi-source environments, integrating these technologies with Airflow, dbt, and Kafka to handle complex data engineering tasks.

Initialize a project through a quick start process that sets up a scalable data platform. This includes deploying a sample pipeline, defining data contracts, and configuring governance checks for immediate DataOps implementation.