Lead Data Architect

Revolutional, LLC•McLean, VA

62d•$160,000 - $225,000•Hybrid

About The Position

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.

Requirements

U.S. Citizenship with the ability to obtain a Public Trust
10–14+ years of experience in data architecture, enterprise data engineering, or large-scale modernization initiatives
Proven experience designing enterprise data architectures for large-scale, distributed systems environments
Experience operating within Agile and SAFe/scaled Agile delivery frameworks
Ability to obtain and maintain a Public Trust clearance
Strong experience designing enterprise data ecosystems including data lakes, warehouses, marts, and distributed data platforms
Experience with large-scale data ingestion, ETL/ELT pipelines, analytics, dissemination, and real-time processing architectures
Experience implementing metadata management, lineage, catalogs, and governance frameworks
Experience with system-of-systems integration, APIs, interface management, and distributed architectures
Experience supporting AI/ML-enabled environments including MLOps, ML pipelines, model deployment, and AI governance
Experience with open-source and modern data stack technologies including Spark, Kafka, Airflow, Databricks, and Snowflake
Experience implementing data governance, data quality, data classification, tagging, privacy, and enterprise sharing frameworks
Experience with cloud-native data services across AWS and Azure environments
Experience implementing DevSecOps practices, CI/CD pipelines, and infrastructure automation
Strong understanding of federal security and compliance frameworks including NIST, FedRAMP, Zero Trust, encryption, access controls, and ATO support
Experience with FinOps, cloud cost optimization, and performance tuning of enterprise data platforms
Experience supporting operational monitoring, maintenance, and lifecycle management of enterprise data systems
Strategic thinker capable of translating mission and business requirements into scalable enterprise data architectures
Strong ownership mindset with accountability for architecture, governance, and operational outcomes
Ability to influence technical direction across engineering, analytics, architecture, and operational teams
Strong decision-making skills balancing modernization goals, operational realities, and compliance requirements
Effective communication across technical, operational, and executive stakeholders
Ability to coordinate delivery and governance across complex, multi-team, multi-contractor environments

Nice To Haves

Certifications in cloud data platforms, big data technologies, or enterprise architecture frameworks
Experience supporting statistical and similarly large-scale federal modernization programs
Experience with large-scale real-time analytics or event-streaming environments
Experience implementing enterprise AI governance or advanced analytics frameworks
Experience supporting DataOps or platform engineering initiatives

Responsibilities

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 FinOps 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)
Establish and enforce architecture governance, standards, and best practices across Agile and SAFe delivery teams
Mentor architects and engineering teams while promoting consistency, governance, and technical excellence