senior-data-engineer

To build scalable data pipelines, you orchestrate complex data workflows using tools like Airflow, Spark, and dbt. This involves automating data ingestion, transforming it with Python or SQL, and loading it into a data warehouse for analytics while ensuring data quality.

The best way to automate ETL workflows and validate data quality is by implementing DataOps practices. You orchestrate pipelines using Airflow, apply data quality checks at each transformation step, and optimize performance with Spark or dbt to ensure data integrity.

Yes, this data engineering approach fully supports modern data stack components. It integrates Kafka for real-time data streaming, dbt for data modeling and transformations, and Spark for large-scale data processing within your pipeline architecture.

To optimize ETL performance, you tune data processing jobs by leveraging Spark for distributed computing and dbt for efficient SQL transformations. This approach scales your data infrastructure, reduces latency, and handles large volumes of data smoothly.

You should implement DataOps when managing complex data workflows that require strict data governance and quality assurance. It automates pipeline orchestration, validates data integrity across ETL systems, and maintains robust production-grade data infrastructure.