Data Engineer (5626)

SMX

11h•$95,500 - $159,100

About The Position

SMX is hiring a Data Engineer responsible for architecting, building, testing, and maintaining the data infrastructure for our mission partners. You will be responsible for a mix of data modeling and backend data mesh work, including creating data queries and performing SQL work. The ideal candidate will analyze and organize raw data, build robust data systems, and explore ways to enhance data quality and reliability. This role involves collaborating with analytics and business teams to transform raw information from different sources into high-quality, accessible data pipelines.

Requirements

Bachelor's degree in Computer Science, - Information Technology, Data Engineering, or a related field.
3 years of professional experience in a data engineering or backend software engineering role.
Active Top Secret security clearance.
Strong proficiency in SQL and data query creation.
Hands-on experience with AWS services, specifically AWS Glue, Glue Catalog, and Step Functions for data integration and workflow automation.
Experience with data modeling, data migration, and ETL functions.
Excellent communication skills, both written and verbal, with the ability to interface with customers

Nice To Haves

Experience supporting AI/ML pipelines, vectorization, or embedding generation
Experience with data governance, access controls, or sensitive data protection
Exposure to DevOps/CI-CD patterns for data workflows
Experience with Databricks
Experience with real-time data streaming and event-driven architectures

Responsibilities

Design, construct, test, and maintain highly scalable data management systems and robust ELT/ETL pipelines.
Build the infrastructure required for optimal extraction, transformation, and loading of data from various sources using cloud technologies, with a strong focus on AWS Glue, Glue Catalog, and Step Functions.
Support data migration efforts and ETL functions.
Optimize database schemas and build systems to collect, validate, and prepare data.
Combine raw information from different sources to meet business and mission needs.
Work with analytics and business teams to understand data requirements and deliver production-ready datasets