Key Responsibilities:

• Data Architecture & Pipeline Engineering: Architect, build, and operate resilient data pipelines integrating Salesforce, SAP, and service data into highly governed analytical and operational data products. Lead the technical implementation utilizing Microsoft Fabric, Dataverse, Python, and SQL.
• AI Context Management: Design and own the context layer for AI agents. Deliver curated, versioned data products with strict data contracts, entity resolution, and optimized retrieval interfaces to ground LLM/AI outputs.
• Intelligent Feedback Loops: Establish sophisticated feedback systems to capture agent outcomes, human corrections, and downstream results, seamlessly feeding them back into data products, prioritization models, and scoring logic.
• Advanced Reporting & Analytics: Oversee the delivery of robust Power BI dashboards, semantic models, and datasets covering critical agent performance, pipeline/funnel metrics, and data quality.
• Data Quality, Governance & Security: Set the standards for and implement robust data-quality frameworks (completeness monitoring, deduplication, entity resolution, and source reconciliation). Define and enforce data lineage, access controls, retention, and compliance protocols in close partnership with IT and data owners.
• Integration Evolution: Drive the modernization of our data landscape by transitioning legacy, file/export-based flows toward secure, modern, API-based, in-perimeter integration patterns.
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Role Requirements

• Experience & Autonomy: 6+ years of hands-on data engineering experience, with a proven track record of owning end-to-end data architectures and production pipelines supporting operational (not just analytical) enterprise use cases.
• Technical Stack Expertise: Deep expertise across the Microsoft data stack (Fabric, Dataverse, Power BI) alongside advanced Python and SQL development skills.
• Integration Expertise: Proven experience integrating complex enterprise data from Salesforce and/or SAP, specifically utilizing modern API-based integration patterns.
• Advanced AI Data Engineering: Direct experience designing data modeling/entity resolution frameworks across CRM/ERP domains, as well as designing context/retrieval layers (e.g., RAG systems) and feedback loops for AI/LLM applications.
• Leadership & Stewardship: Proven ability to make independent architectural and tooling decisions, set technical standards for engineering teams, and ensure the absolute reliability of data feeding customer-facing AI agents.
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