senior-data-engineer

Build scalable data pipelines by orchestrating data ingestion and transformation using Python, Spark, and Airflow. This approach handles large volumes of data from multiple sources, performs complex transformations, and loads it into a data warehouse for analysis.

ETL transforms data before loading it into the warehouse, while ELT loads raw data first and transforms it within the warehouse. This skill develops robust ETL and ELT processes using dbt and SQL to handle data integration and transformation efficiently.

Yes, Airflow and dbt work together for pipeline orchestration and data modeling. Airflow manages scheduling and workflow execution, while dbt handles SQL-based data transformations and modeling within a modern data infrastructure stack.

Spark and Kafka are required for large-scale data processing and complex data architecture design. Spark handles distributed data transformations, while Kafka enables real-time data streaming and ingestion across the end-to-end data lifecycle.

Manage DataOps and the end-to-end data lifecycle by building a modern data stack with Airflow, dbt, and Kafka. This approach ensures robust pipeline orchestration, scalable data integration, and reliable infrastructure for continuous data delivery.

Data modeling processes fail when SQL transformations lack structured orchestration and scalable infrastructure. Implementing dbt for data modeling alongside Airflow for pipeline orchestration ensures complex transformations are executed reliably and maintainably.