Data Engineer

About the role

COMPANY OVERVIEW

For over 25 years, Edgesource Corporation has served as an innovative technology service provider for the Department of War (DOW), Department of Homeland Security (DHS), Department of State (DOS), the U.S. Intelligence Community, Law Enforcement, and other federal, state, and commercial clients locally, nationally, and abroad. From providing boutique technical solutions in support of the DOD Counter Unmanned Aerial Systems (CUAS) mission set to addressing the most critical Cybersecurity threats facing our nation as a prime contractor with the DHS Cybersecurity & Infrastructure Security Agency (CISA), a career at Edgesource is an opportunity to do meaningful, interesting, and impactful work.

POSITION OVERVIEW

Client is seeking a data engineer to grow our team performing cutting edge client work in mission space. With overwhelming amounts of large data, client is seeking a data engineer to support data structuring for implementation into a larger enterprise system that is being custom developed for client.

RESPONSIBILITIES

- Design, develop, and maintain ETL/ELT pipelines for batch and real-time processing using Python and SQL.

- Integrate data from multiple sources, including databases, APIs, streaming platforms, PDFs, and MS Office files.

- Build scalable data architectures to support analytics and machine learning workloads.

- Optimize data processing and queries for performance and cost efficiency in AWS S3.

- Exposure to PySpark or other big data frameworks is a plus for future pipeline scalability.

- Develop and implement web scraping and data ingestion workflows to collect open-source data, integrating content and producing structured datasets and visualizations for analytics and stakeholder consumption.

DATA MANAGEMENT & OPTIMIZATION

- Collect, clean, and validate large volumes of structured and unstructured data.

- Track data versions, implement data quality checks, and ensure data reliability.

- Design and optimize data storage in AWS S3, including raw, intermediate, and final datasets.

- Implement data governance practices, including documentation, cataloging, lineage, and security.

- Ensure compliance security standards.

COLLABORATION WITH DATA SCIENCE & STAKEHOLDERS

- Work closely with Data Scientists, Analysts, and stakeholders to understand data requirements.

- Prepare clean, structured, and feature-ready datasets for analytics and machine learning.

- Support feature engineering, aggregations, and transformations at scale.

- Assist in deploying ML models to production, ensuring monitoring, versioning, and performance optimization.

- APIs, Containers & CI/CD

- Integrate with REST APIs.

- Utilize Docker, Kubernetes, Git, and CI/CD pipelines to deploy and manage workflows.

DOCUMENTATION & COMMUNICATION