Data Architect, AI and Data Practice

• Design and build data warehouse and lakehouse solutions: dimensional modeling, ELT pipelines, performance tuning.
• Deliver reporting and BI: Power BI datasets, DAX, tabular semantic models, report and dashboard development.
• Work directly with client stakeholders to turn loosely stated reporting needs into governed, documented models.
• Own engagements end to end on small teams: architecture through hands-on build through handover. Where the role grows
• Build agent-ready semantic layers: Microsoft Fabric semantic models and Fabric IQ ontologies (entities, relationships, rules) grounded in definitions agreed with the business.
• Carry governance through to agents with Microsoft Purview and Entra ID: sensitivity labels, permissions and row level security that agents inherit.
• Stand up and validate data agents (Fabric data agents, Copilot Studio) against eval suites of known-correct business questions.
• Help shape reusable method and accelerators as we productize these offerings.

Requirements

• At least 15 years of industry experience, including at least 10 years in data and analytics and at least 10 years in consulting or client delivery roles.
• Current Microsoft data platform certifications, at minimum DP-600 (Fabric Analytics Engineer) or PL-300 (Power BI Data Analyst); DP-700 (Fabric Data Engineer) is a plus. These also count toward our Microsoft partner designations.
• Strong SQL and dimensional modeling fundamentals, with production data warehouse delivery experience.
• Deep Power BI: DAX, tabular model design, workspace governance, deployment practices.
• Microsoft data platform experience: Fabric or Synapse, Azure Data Factory or equivalent pipeline tooling, OneLake or lakehouse patterns.
• Experience outside the Microsoft stack, because our clients run mixed estates. Meaningful exposure to at least one of: Snowflake, Databricks, Oracle, AWS analytics services (Redshift, Glue, S3), or dbt.
• Consulting temperament: comfortable in front of clients, able to run a working session, write things down, and manage more than one engagement at a time. Nice to have
• Fabric IQ, semantic layer tooling (dbt Semantic Layer, Cube, AtScale), or knowledge graph and ontology experience.
• Exposure to AI agent tooling: Copilot Studio, Azure AI Foundry, MCP, or LLM evaluation practices.

• Esri ArcGIS or GIS data experience, given our geospatial practice. Why this role? You join at the start of a practice, not the middle of one. The path from this seat is toward the lead semantic architect role as the AI offerings scale, with direct influence on how the offerings, accelerators and standards get built. Small senior teams, no delivery pyramid, and clients who deal with the person doing the work.