What the Role Is
The Data Architect is an invididual contributor position, responsible for partnering across IT and business teams to define, build, and govern Heaven Hill’s enterprise data architecture. This role designs and executes scalable data models and platforms that enable trusted analytics, operational reporting, and reusable datasets, while ensuring strong data integrity, governance, and a great user experience.
The Data Architect will define enterprise-wide data architecture standards and target-state designs to support our EDW modernization to Microsoft Fabric (Warehouse) and Power BI, guiding solution choices, data modeling and integration patterns, and platform conventions to enable scalable, trusted analytics across the business.
In partnership with AI Engineering, the Data Architect will define standards for ingesting and curating both structured and unstructured data for tools like Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP) while working with Security on governed access, so AI solutions are secure, explainable, and used as trusted data.
This role is hands-on (medium to high) in the near term, with a focus on creating standards, reviewing designs, and enabling delivery. Through proven success, it will evolve toward broader governance and enablement as adoption matures.
How You Will Spend Your Time?
- Translate cross-functional business needs into scalable enterprise data architecture (models, integration patterns, and reference architectures)
- Roadmap digital transition so internal and external data sources are useful for self-service analytics and AI tools
- Set and enforce standards for Microsoft Fabric Warehouse (medallion architecture, workspace strategy, naming conventions, reusable templates)
- Own Power BI semantic layer and metric governance (certified datasets, shared dimensions, dataset standards, consistent metric definitions)
- Drive data and trust compliance with Security and governance partners (quality, lineage, access controls, auditability) using Purview and platform controls
- Align ERP master/reference data definitions with analytics needs in partnership with the Accounting Master Data team
- Enable delivery by collaborating with Data Engineering on ingestion/transformation and translating architecture into epics/stories and design review guardrails
- Lead a formal Data Architecture CoP to standardize practices, run design reviews, and manage exceptions
- Define AI data readiness standards (structured + unstructured ingestion), governed RAG patterns, and MCP-based data/tool access in parntership with AI Engineering; advice Security/AI Center of Excellence on controls. Implementation delivered in partnership with AI Engineering and Security
- Evaluate 3rd party solutions for Governance, Master Data Management, AI Agents, etc.
Who You Are…