senior-data-engineer

Build scalable data pipelines by applying DataOps practices, ETL/ELT design, and pipeline orchestration using Python, SQL, Spark, and Airflow to ensure reliability and observability for production analytics and ML workloads.

Design an end-to-end data stack using Kafka for streaming ingest, dbt for data modeling, and automated quality checks to validate data and deliver model-ready features for analytics platforms.

Orchestrate ETL systems and maintain data quality across cloud data stacks by leveraging Airflow for pipeline orchestration, dbt for transformations, and DataOps practices for governance and observability.

Yes, you can use dbt for data modeling and transformations alongside Spark for processing batch and real-time data, ensuring robust data infrastructure and pipeline scalability across your stack.

Apply DataOps practices like automated quality checks, pipeline observability, and validation protocols to ensure security, governance, and reliability across your data infrastructure and ETL systems.