Enterprise Data & AI Architect

About the role

The ideal candidate will live anywhere in the United States but have the ability to travel to our corporate location as needed.

Responsibilities

• Data Architecture & Warehousing:
  • Own and extend a SQL Server analytics data warehouse with dimensional modeling (star schema, conformed dimensions, fact/dimension tables).
  • Design and maintain data pipelines from multiple legacy source systems (ERP, logistics, workflow management) through to Microsoft Fabric.
  • Handle complex temporal data challenges—reconciling multiple systems with incompatible time grains.
  • Maintain schema governance, documentation, and data lineage.
• Business Intelligence & Reporting:
  • Build and maintain Power BI semantic models deployed to Microsoft Fabric.
  • Develop DAX measure libraries for financial and operational reporting.
  • Deliver executive dashboards—P&L, Balance Sheet, Flash Reports, cost analysis, operational metrics.
  • Ensure data quality through validation and reconciliation between source systems and reporting layers.
• AI & Automation:
  • Design and orchestrate AI agent workflows for business process automation.
  • Build workflow automations for data pipelines, alerting, and system integration.
  • Implement agent-assisted analytics—where AI investigates, subject matter experts validate, and the system learns.
  • Manage prompt engineering, AI governance, and safety boundaries for production AI systems.
• Manufacturing Cost Intelligence:
  • Work with legacy ERP cost models (Activity-Based Costing, BOM structures, GL interaction).
  • Build margin analysis, vendor spend tracking, and cost-per-unit modeling.
  • Reconcile estimated costs against actuals across multiple systems.
• Stakeholder Delivery:
  • Translate business needs from executives (CFO, CRO, COO) into data products.
  • Manage a portfolio of concurrent analytics projects from intake through delivery.
  • Deliver iteratively—prove value in days, not months.
• Team Leadership:
  • Lead and mentor a small BI/Analytics team.
  • Distribute work to eliminate single-person bottlenecks and knowledge concentration.
  • Build team capability so institutional knowledge compounds across people, not in one person.
  • Ensure IT governance: proper secrets management, access controls, and security practices.

Requirements

• Qualifications:
  • 5+ years SQL Server/T-SQL—complex analytical queries, views, stored procedures, dimensional modeling.
  • 3+ years Power BI/DAX—Semantic model development, DAX measures, evaluation context. Experience deploying models to production environments.
  • Microsoft Fabric or equivalent cloud analytics platform—Data warehouse, pipelines, semantic models, workspace governance. Azure Synapse, Databricks, or Snowflake experience translates well.
  • Data Warehouse Design—Dimensional modeling, star schemas, ETL/ELT patterns, slowly-changing dimensions.
  • You've built and maintained a production data warehouse.
  • Python—Data transformation, scripting, and automation. Experience with data science libraries (pandas, scikit-learn) is a plus.
  • Git Version Control—All code, models, and documentation are version-controlled. Comfortable with collaborative development workflows.
  • API Integration—Rest APIs, authentication flows, webhook patterns. You'll connect ERP, logistics, and cloud services.
  • Strongly Preferred AI/LLM experience—Hands-on with Claude, OpenAI, or similar. Understanding of prompt engineering, agent orchestration, or agentic AI patterns.
  • Workflow Automation Platforms—n8n, Azure Logic Apps, Power Automate, Zapier, or Make. Building automated data flows and business process automation.
  • Manufacturing or industrial ERP systems—Any background with ERP platforms in a manufacturing context (SSAP, Oracle, Infor, Epicor, Amtech or similar). The specific system is learnable; the domain intuition is not.
  • Financial reporting concepts—GL structure, P&L, Balance Sheet, Cost Accounting. You'll partner closely with Finance.
  • PowerShell—Infrastructure scripting, automation, and diagnostics in a Windows/Azure environment.
  • Azure AD/Identity Management—Service accounts, credential lifecycle, gateway authentication.
• Nice to Have:
  • Vector database experience (Chroma, Pinecone, Weaviate).
  • Data Vault 2.0 methodology.

What You Don't Need to Know (We'll Teach You)

• The specific ERP system and its data structures—well-documented, and you'll have AI-assisted tooling to explore them.
• The specific AI agent framework—understand the fundamentals of LLMs and prompt engineering, you'll pick up our implementation quickly.
• The history of how we got here—what matters is where we're going.