senior-data-engineer

Build scalable data pipelines by orchestrating ETL/ELT workflows using Python, SQL, and Spark. This Skill automates data ingestion, transformation, and loading into data warehouses, optimizing performance and ensuring data quality for production-grade AI/ML systems.

DataOps applies to data pipeline orchestration by integrating data quality validation, governance, and automated workflow management. It ensures data integrity and accuracy throughout the pipeline using tools like Airflow and dbt to manage complex data workflows.

Yes, this data engineering skill works with Kafka for real-time data processing. It supports distributed computing and real-time pipeline construction alongside Python, SQL, Spark, and Airflow to handle streaming data workflows.

Optimize ETL/ELT workflows and ensure data quality by implementing DataOps patterns, performance tuning, and data validation checks. This Skill enhances transformation efficiency while validating data integrity at each pipeline step using dbt and Airflow.

Yes, you can use this to design data architecture and implement data governance. It covers data modeling, pipeline orchestration, security, and cost optimization to establish robust data infrastructure for business intelligence.