Develop and maintain ETL pipelines supporting structured and semi-structured data ingestion and transformation.
Support batch and real-time data workflows across enterprise data platforms.
Work with large datasets to identify patterns, trends, and data quality issues.
Build and maintain data transformation processes using Python, SQL, and modern data engineering tools.
Support workflow orchestration and scheduling using tools such as Apache Airflow or similar technologies.
Perform data quality checks, data linkage, and integration across multiple data sources.
Collaborate with cross-functional teams including data scientists, analysts, and system engineers.
Assist in the development of data models and optimized data structures for analytics and reporting.
Maintain documentation for data pipelines, workflows, and data architecture.
Contribute to cloud-based data environments supporting scalable and secure data operations.

U.S. Citizenship required.
Bachelor’s degree in Computer Science, Information Systems, or related field.
Public Trust required or ability to obtain.
3+ years of professional experience in data engineering, data integration, or data platform development.
Strong proficiency in Python and SQL for data transformation and automation.
Experience with ETL frameworks and data pipeline development.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
Understanding of data modeling, data warehousing, and distributed data processing concepts.
Experience working with large datasets and enterprise data systems.

Mid Data Engineer

Key skills