Description

You will design and build scalable, cloud-native data platforms.

Responsibilities

• Design and build scalable, cloud-native data platforms using PySpark and Python.
• Create robust, maintainable ETL/ELT pipelines integrating diverse systems.
• Mentor engineers, reviewing code and sharing knowledge to foster growth.
• Own and optimize cloud infrastructure for data workflows, storage, and processing.
• Champion data engineering best practices, including testing and performance tuning.

Required Skills

• 10+ years of professional experience in data engineering.
• Expertise in PySpark and Python.
• Proven experience building ETL/ELT Pipelines.
• Strong working knowledge of Apache Airflow and DAGs.
• Experience with AWS Lake Formation, Glue, and Athena.
• Proficiency in Data Pipelines design and implementation.
• Familiarity with AWS services including SQS, DLQ, and Lambda.
• Experience with data governance practices and lineage.
• Ability to decompose complex problems into modular components.

Key Skills

Education

Any Graduate