Sr. Data Architect

SteerBridge Strategies is a modern technology company delivering innovative, mission‑focused solutions to the U.S. Government and private sector. Leveraging deep expertise in federal acquisition, digital transformation, and emerging technologies, we deliver agile, commercial‑grade capabilities that accelerate operational effectiveness and drive measurable mission success. At the core of SteerBridge is our people—especially the veterans whose leadership, problem‑solving mindset, and commitment to excellence elevate every project we support. We don’t simply hire exceptional talent; we cultivate it, creating meaningful career pathways for veterans, military spouses, and professionals who share our passion for advancing technology and strengthening the missions we serve. We are seeking a Senior Data Architect to lead the design and evolution of enterprise-level data ecosystems. You will be responsible for architecting scalable, secure, and high-performance data infrastructures that support mission-critical federal programs, including VA claims processing and aviation sustainment (F-35/C-130). This is a "player-coach" role that requires high-level strategic planning alongside hands-on engineering execution. Key Responsibilities Architecture & Design: Design conceptual, logical, and physical data models for complex federal environments. Lead the transition from legacy on-premises systems to modern, cloud-native (AWS/GCP) data platforms. Pipeline Development: Architect and oversee the build of automated ETL/ELT pipelines using Python, SQL, and PySpark to ingest and transform unstructured and structured data. Cloud Data Warehousing: Implement and optimize enterprise data warehouses using tools like AWS Redshift, Google BigQuery, AWS Glue, and Databricks. Governance & Compliance: Establish data governance frameworks, metadata management, and data lineage in alignment with federal standards (HIPAA, FHIR, NIST). Performance Optimization: Conduct index/partition design, query tuning, and sharding strategies to ensure high availability and scalability for real-time analytics. AI/ML Support: Design data architectures that facilitate AI/ML initiatives, including model training pipelines and real-time inference in production environments. Leadership: Mentor a team of data engineers, enforce software engineering best practices (CI/CD, unit testing, documentation), and serve as a technical bridge between stakeholders and delivery teams.