Data Engineer

Job Description

Job Description

Overview

Join a team where innovation meets mission. Our AI, cloud, cyber, and modernization solutions save agencies thousands of hours, safeguard national security, and strengthen health and humanitarian missions worldwide. With 1,700+ team members, 1,500+ AI / data experts, and 100+ prime contracts, we deliver at scale and with purpose.

We’ve been recognized as a Top Workplace by the Washington Post for six straight years and named to the Inc. 5000 Fastest Growing Private Companies 13 of the past 14 years. Credence is a welcoming home for those looking to grow and contribute to positive change. We encourage all employees to expand beyond their boundaries, dive into important world-changing Federal challenges.

Position Summary

Credence has an immediate need for a Mid-Level AI Data Engineer to join our growing AI and Automation practice. You will be a technical anchor in our AI and Automation practice. You’ll apply foundational AI skills to build and deploy data-driven solutions. Under mentorship from senior AI leaders, you’ll drive agentic AI development lifecycles and collaborate across engineering, data, and stakeholder teams to deliver high-impact, cloud-native AI capabilities that advance federal missions.

Responsibilities include, but are not limited to the duties listed below

• Data Integrations for Generative AI & LLM Usage

Build and optimize data pipelines that prepare, clean, and structure data for generative AI and LLM usage.

Manage and organize large datasets across cloud platforms (e.g., AWS, Azure, GCP) using data lake and warehouse technologies. Implement medallion architecture (Bronze / Silver / Gold layers) to ensure data quality, lineage, and accessibility.

Work with both SQL and NoSQL systems to model, query, and load large-scale datasets. Monitor, tune, and maintain high-performance data stores supporting analytics and reporting.

Work alongside data engineers, software engineers, and data scientists to develop operational agentic AI systems.

Help automate model deployment workflows using Infrastructure as Code (IaC), CI / CD pipelines, and container orchestration tools.

Monitor AI systems post-deployment, perform performance tuning, and apply best practices for reliability and scalability.

Write clean, well-documented code following industry and federal guidelines, support reproducible development.

Stay current on AI / ML trends and tools and actively learn from senior team members through mentorship and technical design reviews.

Requirements

Preferred

Why This Role Matters

Benefits