• Define enterprise architecture strategy across enterprise applications, integrations, APIs, Azure cloud platforms, enterprise data, analytics, automation, and AI enablement. • Establish scalable architecture patterns enabling digital customer and employee experiences across CRM, web, mobile, communications, and operational platforms. • Define enterprise engineering and platform standards across APIs, integrations, cloud services, observability, reusable services, technical design patterns, and platform reliability. • Promote reusable enterprise capabilities and scalable platform approaches that accelerate delivery and reduce technical fragmentation. • Serve as a trusted advisor to business and technology leadership on enterprise technology direction, modernization priorities, and technical investment decisions. • Own and evolve enterprise integration and platform strategy including APIs, middleware, orchestration, interoperability, and event-driven architecture. • Guide architecture and engineering direction across Microsoft Dynamics 365 (CE/F&O preferred), ERP, CRM, operational systems, digital platforms, and third-party ecosystems. • Improve observability, resiliency, monitoring, supportability, and enterprise reliability. • Guide modernization of legacy systems and synchronization approaches into scalable, maintainable cloud-native patterns. • Partner with Digital Solution Delivery, ERP, and Infrastructure leaders to enable scalable digital experiences through reusable enterprise services and shared data models. • Establish pragmatic enterprise data governance practices that improve trust, quality, consistency, accessibility, and usability of enterprise data without creating unnecessary bureaucracy. • Enable trusted enterprise data, analytics, automation, and AI-ready architectures leveraging Microsoft Fabric, Snowflake, Databricks, and Azure data services.

• Develop and maintain enterprise data and analytics strategy documents, roadmaps, and improvement plans (including POA&Ms) for DLA J6TF • Design and support an enterprise data governance framework — governance structure documentation, data ownership assignments, data stewardship roles, and operating procedures • Manage metadata per DoD standards, including DoD Metadata Registry (DoD 8320 series) and Net-centric Enterprise Services requirements • Provide architecture recommendations for DLA analytical platforms — data ingestion, storage, processing, and delivery layers • Support enhancement of DLA's Enterprise Data Warehouse (EDW) — logical and physical schema design, data source integration, and analytical layer development • Develop data dictionaries, data inventories, and data lineage documentation across DLA's multi-system enterprise environment • Identify and register authoritative data sources across DLA data domains; document authoritative source designations per governance policy • Conduct data quality assessments — profile source data, document quality issues, and produce remediation recommendations • Support data governance working groups and data stewardship council activities — facilitation, briefings, and deliverable preparation • Develop and maintain artifacts including logical data models, conceptual data models, metadata management plans, and enterprise architecture alignment documents • Prepare monthly progress reports, IPR briefing charts, and deliverables for COR review

• Create and maintain optimal data pipeline architecture • Assemble large, complex data sets that meet functional / non-functional business requirements • Design and implement data application components • Work with data and analytics experts to strive for greater functionality in our data systems • Develop and direct security procedures and safeguards to reduce the risk of outside breaches and protect sensitive information

• Serve as the primary technical lead and escalation point for enterprise data engineering initiatives. • Bridge business requirements, architectural standards, and engineering implementation. • Partner with business analysts, architects, BI teams, DevOps, and data engineers to support successful solution delivery. • Interpret and clarify technical implementation requirements for data engineering teams. • Guide implementation decisions across Databricks pipelines, transformations, and data models. • Review engineering implementations for consistency, scalability, maintainability, and alignment to standards. • Support troubleshooting and root cause analysis for data quality issues, failed pipelines, performance concerns, and production defects. • Act as L1/L2 support lead for enterprise data platform operational issues. • Perform lineage and downstream impact analysis for data model and pipeline changes. • Guide implementation of reusable engineering patterns, medallion architecture, and gold-layer datasets. • Coordinate defect triage, release support, deployment validation, and production stabilization activities. • Support adoption of engineering standards, CI/CD processes, governance controls, and operational best practices. • Mentor and guide data engineers on technical implementation approaches and enterprise standards. • Drive consistency across engineering teams, platforms, and data products. • Document technical patterns, implementation standards, operational procedures, and support processes.