Data Product Enablement Specialist

Anduril Anduril · Defense · Costa Mesa, CA · Corporate Technology : Strategic Operations and Transformation : Master Data Management

The Data Product Enablement Specialist will champion the adoption and utilization of an enterprise semantic layer, bridging business intelligence needs and technical data solutions. This role supports data scientists, application developers, and business analysts in leveraging the semantic layer for application building, analytics, and AI initiatives. Responsibilities include contributing to semantic layer design, data definition, governance, leading adoption programs, creating learning resources, consulting on AI-driven solutions, and communicating strategic value.

What you'd actually do

  1. Contribute to Semantic Layer Design, Data Definition & Governance: Actively participate in the design and definition of the enterprise semantic layer, collaborating with Data Architecture, Data Platform, MDM, and business teams. This includes providing prioritization guidance on the development of the golden record based on business need, advising on semantic model structures, critical attributes on data entities, data relationships, and business logic within Anduril's Ontology. Partner with SMEs to build comprehensive data dictionaries and inventories. As a key member of the MDM CoE, contribute to the development and enforcement of data governance policies and data quality standards, while continuously gathering user feedback and collaborating closely with MDM, Data Platform teams, and relevant platform product teams (e.g., Palantir Foundry) to prioritize and implement semantic layer enhancements.
  2. Champion Semantic Layer Adoption & Operational Enablement: Lead the charge for semantic layer adoption, acting as the primary advocate and subject matter expert for all user personas. This includes designing and delivering comprehensive enablement programs (workshops, bootcamps, office hours, hackathons, documentation, and learning resources) and managing day-to-day operational workflows like routing complex issues, triaging support questions, and overseeing user access provisioning and security group management in close collaboration with InfoSec and Compliance.
  3. Create Intuitive Learning Resources & Decentralize Support: Develop and maintain high-quality training materials, documentation, best practices guides, and reusable usage patterns to empower users for self-service data consumption. Identify, cultivate, and equip power users across business domains to serve as local semantic layer champions, accelerating peer-to-peer learning and reducing reliance on centralized support. Build a scalable enablement ecosystem where knowledge compounds organically across teams.
  4. Consult & Facilitate AI-Driven Solutions: Provide guidance and direct support to data scientists, application developers, and business analysts on how to effectively query, integrate with, and maximize value from the semantic layer. Directly enable development teams by showcasing optimal methods for consuming semantic/gold layer data.
  5. Translate & Evangelize Strategic Value: Communicate the strategic value and practical benefits of the MDM-fueled semantic layer to diverse audiences, from engineers to executive stakeholders, clearly articulating how it accelerates innovation and ensures data trust, aligning with business objectives and IPO readiness.

Skills

Required

  • Minimum of 6 years of progressive experience in Data Enablement, Data Analytics, Data Architecture, or Data Product roles, with a strong focus on user empowerment and semantic layer design.
  • Exceptional ability to operate as a bridge between business and technical teams, translating complex business problems into clear data requirements, semantic model specifications, and data dictionary definitions that directly shape the enterprise golden record and ontology.
  • Proven hands-on experience leveraging data platforms for data modeling, governance, and consumption patterns.
  • Proven experience with Master Data Management principles and their application in building trusted data foundations, including data quality, governance, stewardship, and data lifecycle management.
  • Familiarity with modern data platforms and tools (e.g., SQL, Python, Tableau, data modeling) and their integration points with semantic layers.
  • Demonstrated experience designing, developing, and delivering engaging technical training programs

What the JD emphasized

  • AI-driven innovation
  • AI initiatives
  • AI-driven application development