senior-data-engineer

To build scalable data pipelines, you orchestrate complex ETL workflows using Airflow to automate execution and scheduling, while leveraging Spark to optimize data processing jobs for maximum efficiency and minimal resource consumption.

Validating data quality in an ELT pipeline involves implementing rigorous checks for schema, constraints, and statistical anomalies to ensure data integrity and governance at each transformation stage.

Optimizing Spark data processing jobs for terabyte-scale datasets involves tuning transformations for maximum efficiency and minimal resource consumption, ensuring scalable pipeline orchestration handles daily ingestion reliably.

Yes, this approach supports building production-grade data architecture with Kafka and dbt, addressing pipeline orchestration, data quality assurance, and DataOps implementation for scalable, reliable infrastructure.

You need DataOps implementation for your data infrastructure when solving challenges in data architecture design and pipeline orchestration to satisfy requirements for production-grade data systems, performance optimization, and data governance.

To start orchestrating data pipelines, you configure the workflow to read input data from the 'data/' directory, process transformations, and output the computed results to the 'results/' directory.