Lead Data Architect

Lead Data Architect

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 DevSecOps 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.

Responsibilities include:

• 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

Technical environment includes:

• Cloud-native data platforms (AWS, Azure)
• Data lakes, lakehouse architectures, warehouses, marts, and distributed analytics ecosystems
• Big data and streaming technologies (Spark, Kafka, Databricks, Snowflake, Airflow)
• APIs, event-driven architectures, and distributed integration platforms
• ETL/ELT pipelines and large-scale data processing frameworks
• MLOps, AI/ML-enabled analytics, and model deployment environments
• Infrastructure-as-Code and automation tools (Terraform)
• DevSecOps pipelines and CI/CD automation frameworks
• Data governance, metadata, lineage, and catalog platforms
• Agile and scaled Agile (SAFe) delivery environments
• Delivery and collaboration platforms (Git, Jira, Confluence)

Baseline requirements include:

• 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

Technical capabilities include:

• 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

Core strengths include:

• 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 have (differentiators) include:

• 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