Design, develop, and maintain scalable data pipelines and ETL/ELT processes across AWS cloud environments.
Manage and optimize data platforms using services such as AWS Glue, Redshift, Athena, S3, Glue Data Catalog, Lake Formation, and IAM.
Implement and orchestrate workflows with Apache Airflow for large-scale data processing and analytics.
Build and optimize data warehouse solutions and business-facing analytical models using dbt, Spark, and SQL.
Collaborate with cross-functional teams to deliver secure, scalable, and highly available data solutions.
Support CI/CD pipelines and deployment automation for data platform workflows, especially with CodeBuild, Jenkins, and GitHub Actions.
Architect and maintain lakehouse storage layers on S3 using Apache Iceberg and/or Delta Lake table formats.
Manage AWS Glue Data Catalog, crawlers, and Lake Formation permissions for data governance and access control.

5+ years of experience in data engineering, data platform, or cloud data engineering roles with a strong focus on AWS data services.
Hands-on expertise with AWS Glue (PySpark), Redshift, Athena, S3, Glue Data Catalog, Lake Formation, and IAM.
Production experience with Apache Airflow, including DAG development, scheduling, and multi-environment management.
Experience building data warehouse solutions and business-facing analytics models using dbt, Spark, and SQL.
Strong Python skills for building ETL/ELT pipelines and automation tooling.
Familiarity with lakehouse architectures using Apache Iceberg or Delta Lake on S3.

Senior Data Engineer – Cloud, AWS

Key skills