Data Engineer

Edgesource•Chantilly, VA

14d•Onsite

About The Position

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. Development is performed on client site.

Requirements

3-5 years + of professional experience in data engineering or related roles.
Strong collaboration skills to work effectively with Data Scientists, Analysts, and Engineering teams.
Ability to communicate complex technical concepts to non-technical stakeholders.
Detail-oriented, curious, and committed to data quality.
Capable of managing multiple priorities in a fast-paced environment.
Python, SQL, and PySpark (highly desired) for data processing and pipeline development.
Elastic/OpenSearch for search and analytics solutions.
Experience with AWS cloud services and Linux environments.
Git for version control and collaborative development.
Understanding of machine learning workflows and MLOps concepts.

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.
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.
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.
Document pipelines, data schemas, and transformations clearly.
Communicate technical concepts effectively with cross-functional teams.
Participate in code reviews and promote best practices across the team.