Design, develop, and maintain scalable data pipelines using AWS services.
Build and optimize ETL/ELT workflows for structured and unstructured data.
Implement data lakes and data warehouses on AWS.
Work with large datasets to ensure high performance, reliability, and data integrity.
Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements.
Perform data modeling for analytical and operational use cases.
Ensure data quality, governance, and security best practices.
Monitor and troubleshoot data workflows and production issues.
Support CI/CD and automation for data platform deployments.

Strong hands-on experience with AWS services such as S3, Glue, Redshift, Lambda, EMR, Athena, and RDS.
Proficiency in Python and SQL for data processing and analysis.
Experience in building and optimizing ETL/ELT pipelines.
Solid understanding of data warehousing and data lake architecture.
Experience with Apache Spark / PySpark.
Knowledge of workflow orchestration tools (Airflow or similar).
Familiarity with streaming frameworks (Kinesis/Kafka) is a plus.
Experience with data modeling and performance tuning.
Understanding of DevOps, CI/CD, and infrastructure as code (Terraform/CloudFormation).
Experience working in Agile/Scrum environments.

Data Engineer

Key skills