Sr. Data Architect

SteerBridge 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.

SteerBridge seeks a highly skilled and motivated individual to join our team as a Senior Data Architect to own the enterprise data architecture strategy for our Modern Disability Claims program. This role sets the technical direction for how claims, clinical, and healthcare data is modeled, stored, integrated, and governed across our systems, ensuring our data infrastructure scales to support claims adjudication, analytics, and AI/ML initiatives.

In this role, you will completely own our data architecture domain — translating ambiguous business and mission requirements into clear, scalable technical designs, resolving complex cross-system data challenges, and mentoring engineers and junior architects. The ideal candidate is a strategic thinker, an excellent communicator, and has a track record of designing data platforms that hold up at scale.

Data Architecture and Modeling

Designing conceptual, logical, and physical data models for enterprise-scale systems.

Establishing data modeling standards and best practices across teams (dimensional modeling, data vault, star/snowflake schemas).

Expertise in master data management and enterprise data architecture patterns.

Balancing OLTP and OLAP workload design across the platform.

Project Experience: Defined and rolled out an enterprise data modeling standard adopted across multiple product teams, improving consistency and reducing rework.

Enterprise Data Platforms and Cloud Strategy

Selecting and architecting cloud-native data platforms (data lakes, data warehouses, data mesh) on AWS, GCP, or Azure.

Designing multi-cloud or hybrid-cloud data strategies where appropriate.

Establishing cost governance and performance standards for large-scale data workloads.

Evaluating and recommending emerging data platform technologies.

Project Experience: Led the architectural design for a cloud data platform migration, defining the target-state architecture that guided the engineering team's implementation.

Data Integration and Pipeline Strategy

Defining integration patterns and standards for ETL/ELT pipelines across structured and unstructured sources.

Architecting for both batch and real-time data integration needs.

Setting standards for schema evolution, data versioning, and pipeline observability.

Project Experience: Designed the integration architecture connecting multiple claims systems of record into a unified analytics layer.

Data Governance, Security, and Compliance

Establishing enterprise data governance frameworks, including data cataloging, lineage, and metadata management standards.

Designing role-based access control and data protection architecture for sensitive healthcare and claims data.

Ensuring architecture aligns with relevant compliance frameworks (e.g., HIPAA, federal data handling requirements).

Project Experience: Implemented a data governance and lineage framework that gave stakeholders auditable visibility into claims data flows.

AI/ML and Analytics Enablement

Architecting data platforms and pipelines to support AI/ML and advanced analytics initiatives.

Partnering with data science teams to design feature stores and ML-ready data infrastructure.

Evaluating architecture implications of emerging AI/ML tooling.

Project Experience: Designed the data architecture underpinning an AI/ML claims-adjudication initiative, enabling reliable training and inference pipelines.

Leadership and Stakeholder Collaboration

Leading technical design reviews and architecture review boards.

Mentoring data engineers and junior architects on architectural best practices.

Translating ambiguous business and mission requirements into clear technical roadmaps.

Communicating architectural decisions and tradeoffs to both technical and non-technical stakeholders.

Project Experience: Led cross-functional architecture reviews that aligned engineering, data science, and program stakeholders on a shared technical roadmap.

Must be a U.S. Citizen.

Bachelor's Degree or Above in Computer Science, Systems Engineering, Data Science, or related field.

Must hold, or be able to obtain, a Public Trust clearance (an active Secret or Top Secret clearance also satisfies this requirement).

Minimum 6+ years of experience in data architecture, to include:

Strong experience with Python, SQL, Pandas, PySpark, and Git.

Experience with enterprise data warehousing and lake technologies (Databricks, Redshift, BigQuery, or Snowflake).

Demonstrated ownership of enterprise-scale data architecture within cloud-based ecosystems.

Location: Preferred local to the Vienna, VA area and able to work on-site at our Vienna, VA office (3+ days/week). Hybrid opportunities at supervisors' discretion.

Infrastructure as code and DevOps practices supporting data platform deployment.

Strong documentation and architecture communication skills (diagramming tools, technical writing).

CI/CD familiarity as it relates to data platform and pipeline delivery.