Secret-Cleared Data Engineer for Pipelines & AI/ML

SMX; Dover (DE); On-site; USD 115,000 - 187,000; Full time

Job summary

SMX is seeking a Data Engineer to design, build, and optimize data systems for collection, storage, access, and analytics at scale. The role requires analyzing raw data, building robust pipelines, and collaborating with analytics and business teams to transform diverse sources into high-quality data assets.

You will ensure reliability, security, and accessibility for data consumers. Responsibilities include creating usable pipelines for scientists and applications, maintaining SLA-driven

Qualifications

• Bachelor's degree in computer science, information technology, data engineering, or related field.
• 8 years of professional experience in data engineering or backend software engineering.
• Active Secret security clearance.
• Strong proficiency in SQL and data query creation.
• Experience with data modeling, data migration, and ETL functions.
• Excellent communication skills, both written and verbal, with the ability to interface with customers.

Responsibilities

• Create data pipelines that convert raw data into formats usable by data scientists and other data consumers.
• Make data available, accessible, and secure to stakeholders.
• Ensure data pipelines meet SLAs and performance standards.
• Monitor data repositories and pipelines to ensure operational efficiency and address issues.
• Implement orchestration and automation solutions for data pipelines.

Skills

SQL

Data modeling

Data migration

ETL

Communication

Education

Bachelor's degree in computer science / IT / Data Engineering

Tools

Databricks

CI/CD pipelines

Job description

SMX is seeking a Data Engineer to design, build, and optimize data systems for collection, storage, access, and analytics at scale. The role requires analyzing raw data, building robust pipelines, and collaborating with analytics and business teams to transform diverse sources into high-quality data assets.

You will ensure reliability, security, and accessibility for data consumers. Responsibilities include creating usable pipelines for scientists and applications, maintaining SLA-driven