Lead Data Architect

Revolutional delivers advanced technology solutions and mission support to federal agencies across civilian, health, and national security environments. We apply modern capabilities, including AI/ML, cloud, cybersecurity, and IT modernization to solve complex challenges, enable faster and more secure operations, and drive measurable mission outcomes.

We are redefining how federal technology gets built and delivered by operating with a product mindset, prioritizing speed, ownership, and execution over bureaucracy.

Location: Suitland, MD (Hybrid)

Terms: Full-time

Clearance/Work Authorization: U.S. Citizenship with the ability to obtain and maintain a Public Trust is required

Travel: %

This position supports Revolutional’s federal customer as part of an application transformation and modernization initiative.

This program is driving a large-scale transformation of systems into a data‑centric, cloud‑native ecosystem capable of supporting high‑volume, near real‑time data processing and advanced analytics. The work includes modernization of legacy applications, development of new cloud‑native solutions, and implementation of Dev Sec Ops  and scaled Agile practices across the organization.

The core challenge: orchestrating complex, multi‑contractor delivery while transforming both technology and operating models without disrupting mission‑critical operations.

As a Lead Data Architect at Revolutional, you will define and drive enterprise data architecture strategy, governance, and implementation across a large‑scale federal modernization program.

You will lead architecture efforts spanning data platforms, pipelines, governance frameworks, analytics ecosystems, AI/ML integration, and large‑scale distributed processing environments. You will work across multiple systems, teams, vendors, and contractors to ensure data is structured, governed, secured, integrated, and operationalized effectively across the enterprise.

This role requires someone who can balance long‑term data strategy with operational delivery realities, while establishing architecture standards that support scalability, compliance, resiliency, and real‑time data operations.

• Provide technical leadership across enterprise data architecture efforts within a large‑scale modernization program
• Design and govern scalable data ecosystems including data lakes, lakehouse architectures, data warehouses, marts, and distributed processing platforms
• Define and implement enterprise data models, schemas, standards, retention strategies, and lifecycle management approaches
• Oversee data management, integration, quality, lineage, storage, retention, and governance processes across systems
• Establish metadata management, data catalogs, data dictionaries, and lineage frameworks supporting governance and traceability requirements
• Design and manage large‑scale data ingestion, ETL/ELT pipelines, transformation workflows, analytics, and dissemination capabilities
• Support real‑time and streaming architectures using event‑driven processing and distributed messaging systems
• Design and oversee APIs, system interconnections, interface management processes, and Interface Control Documents (ICDs)
• Support AI/ML‑enabled architectures including ML pipelines, MLOps processes, model deployment, and AI governance frameworks such as the NIST AI RMF
• Collaborate with application architects, engineers, data scientists, SMEs, and external vendors to deliver secure, scalable, and high‑performing data solutions
• Ensure compliance with federal data management, privacy, and security requirements including NIST, FedRAMP, Zero Trust, ATO processes, encryption, access control, and data sharing standards
• Lead architecture efforts supporting system‑of‑systems (SoS) integrations across multiple contractors, vendors, and interdependent platforms
• Implement Fin Ops and cloud optimization strategies including cost monitoring, tagging, performance tuning, and operational efficiency improvements
• Support operational management of enterprise data platforms including monitoring, maintenance, performance optimization, and lifecycle management (O&M)
• Es…