Senior Data Engineer

About the role

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. Credence has an immediate need for a Senior 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 advanced AI and data engineering expertise to design, build, and deploy data-driven solutions. 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. Real-World Impact — Your work will support defense and health agencies where AI solutions directly contribute to national security and public well-being. Growth-Oriented Environment — Learn from technical leaders, innovate within Agentic AI, and mentor those around you in AI best practices. Culture of Empowerment — You'll be part of a team that values innovation, trust, collaboration, and mission success.

Responsibilities

• Data Integrations for Generative AI & LLM Usage: Build and optimize data pipelines that prepare, clean, and structure data for generative AI and LLM usage
• Data Lake & Warehouse Engineering: 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
• Database Management & Performance: 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
• Collaborative Engineering: Work alongside data engineers, software engineers, and data scientists to develop operational agentic AI systems
• Cloud Enablement: Help automate model deployment workflows using Infrastructure as Code (IaC), CI/CD pipelines, and container orchestration tools
• Production Monitoring & Optimization: Monitor AI systems post-de