Description

You will design, build, and maintain end-to-end ETL/ELT data pipelines.

Responsibilities

• Design and implement data lakehouse and warehouse architectures using AWS S3, Glue, and Snowflake.
• Develop parameterized, reusable, and modular ETL components for large-volume data transformation.
• Implement incremental data loads and Change Data Capture (CDC) processes.
• Automate data workflows using orchestration tools for scheduling and dependency management.
• Set up data quality validation frameworks and automated reconciliation checks.

Required Skills

• 8–10 years of hands-on experience in data engineering and ETL/ELT development in cloud environments.
• Strong proficiency in Snowflake, including query optimization and performance tuning.
• Deep expertise in ETL/ELT tools, including AWS Glue, dbt, Informatica, or Talend.
• Experience with the AWS data stack: S3, Glue, Step Functions, Kinesis, and IAM.
• Advanced SQL skills combined with experience in Python, Scala, or Java for data transformation.
• Solid understanding of data modeling techniques (Star Schema, Data Vault 2.0).
• Familiarity with data orchestration, workflow automation, and CI/CD in data environments.
• Experience implementing data validation and quality monitoring frameworks.
• Knowledge of data governance, metadata management, and security practices.

Key Skills

Education

Bachelor's or Master's degrees