Description

Key Skills: ETL, Data Engineering, Python

Roles and Responsibilities:

• Lead the design and implementation of scalable, resilient ELT/ETL pipelines for enterprise analytics and reporting use cases.
• Own data engineering deliverables end-to-end, from requirements through production deployment and post-release support.
• Build and maintain robust pipelines using Python, SQL, Airflow, and cloud-native services.
• Define and lead data testing strategy (unit, integration, regression, and UAT) to ensure data accuracy and integrity.
• Improve production workflow reliability with observability, automation, incident triage, and SLA-driven resolution.

Skills Required:

• 9 - 12 years of experience in data engineering, data design, and analytics platforms; 5+ years hands-on Python and SQL; 4+ years with AWS and Airflow.
• ETL and ELT pipeline development and delivery for analytics-ready data assets.
• Data engineering fundamentals including data modeling, data integrity, and transformation logic.
• Python and SQL for building transformations, troubleshooting, and SQL performance tuning.
• Airflow-based workflow orchestration and cloud deployment on AWS, with Git-based version control and CI/CD.

Good to Have:

• Experience with enterprise analytics and reporting tools (e.g., Tableau, MicroStrategy) and agile delivery environments.

Education: Bachelor's degree in Computer Science, Engineering, or a related field.

Key Skills

Education

Any Graduate