Data Engineer — ETL/Python & AWS Data Pipelines

Edgesource; Chantilly (VA); Presencial; USD 160.000 - 210.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flex Time Off

Hybrid Workplace

401K Program

Paid Parental Leave

Extensive Coverage

Continuing Education

Open Job Opportunities

Descripción de la vacante

Edgesource is seeking a Data Engineer to grow our data team in Chantilly, VA. You will design, build, and optimize ETL/ELT pipelines, ingest data from diverse sources, and enable analytics and ML workflows across enterprise systems.

The ideal candidate contributes to scalable architectures, data governance, and collaboration with data scientists and engineers, while leveraging Python, SQL, PySpark, and AWS in an on-site environment.

Formación

• Active TS/SCI FSP clearance required.
• 3–5 years of professional data engineering experience.
• Strong collaboration and communication with data scientists and engineers.
• Detail-oriented with ability to manage multiple priorities.
• Experience with data governance and security practices.

Responsabilidades

• 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 for future pipeline scalability.
• Develop and implement web scraping and data ingestion workflows to collect open-source data and produce structured datasets.

Conocimientos

Python

SQL

PySpark

OpenSearch/Elastic

AWS

Linux

Git

ML workflows

Herramientas

Docker

Kubernetes

REST APIs

Descripción del empleo

Edgesource is seeking a Data Engineer to grow our data team in Chantilly, VA. You will design, build, and optimize ETL/ELT pipelines, ingest data from diverse sources, and enable analytics and ML workflows across enterprise systems.

The ideal candidate contributes to scalable architectures, data governance, and collaboration with data scientists and engineers, while leveraging Python, SQL, PySpark, and AWS in an on-site environment.