Lead Data Architect

Role overview

Lead enterprise data architecture for a large-scale modernization program handling high-volume, near-real-time data and advanced analytics. The role sets strategy and governance across data platforms, pipelines, integration, security, AI/ML enablement, and operational lifecycle management while coordinating architecture decisions across distributed systems, delivery teams, vendors, and contractors.

Responsibilities

- Define and govern scalable data ecosystems spanning data lakes, lakehouses, warehouses, marts, and distributed processing platforms. - Establish enterprise data models, schemas, standards, retention policies, lifecycle approaches, and controls for data quality and sharing. - Design and oversee ingestion, ETL/ELT, transformation, analytics, dissemination, event-streaming, and real-time processing capabilities. - Implement metadata management, catalogs, dictionaries, lineage, classification, tagging, privacy, and governance frameworks. - Lead APIs, system interconnections, interface management, Interface Control Documents, and system-of-systems integration across dependent platforms. - Support AI/ML architectures, including model pipelines, MLOps, deployment, and governance aligned with recognized risk-management practices. - Drive secure, scalable cloud operations through FinOps, cost monitoring, performance tuning, monitoring, maintenance, and lifecycle management. - Mentor architects and engineering teams while enforcing architecture standards across Agile and scaled-Agile delivery environments.

Requirements

- Ten or more years in data architecture, enterprise data engineering, or large-scale modernization, with experience designing distributed enterprise systems. - Bachelor’s degree plus 12 years of experience, associate degree plus 14 years, or high school diploma/equivalent plus 16 years. - U.S. citizenship and the ability to obtain a Public Trust clearance. - Strong experience with enterprise data platforms, large-scale pipelines, analytics, real-time processing, APIs, and distributed integrations. - Experience with Spark, Kafka, Airflow, Databricks, Snowflake, or comparable modern data-stack technologies. - Experience with AWS and Azure data services, DevSecOps, CI/CD, infrastructure automation, and operational platform management. - Working knowledge of NIST, FedRAMP, Zero Trust, encryption, access controls, authorization-to-operate processes, and federal data-sharing requirements. - Experience delivering within Agile or SAFe/scaled-Agile frameworks.

Nice to have

- Certifications in cloud data platforms, big-data technologies, or enterprise architecture. - Experience with federal modernization, large-scale event streaming, advanced analytics, enterprise AI governance, DataOps, or platform engineering.