Sr. Analytics Engineer

job purpose: The Senior Analytics Engineer will be a foundational developer and force multiplier for fairlife’s ambition to create a best‑in‑class Decision Intelligence (DI) ecosystem. This role will own the development of production‑grade data products (domain models, governed metrics, semantic layers, and self‑serve insights) while also helping the organization adopt and scale AI-enabled decision workflows (agents/copilots) through guardrails, training, enablement, and measurable adoption.

This role is accountable not only for delivering decision intelligence, but also for increasing decision velocity and trust across Finance, HR, Demand, and Site Operations by building durable assets and by coaching others in modern analytics engineering and AI practices.

responsibilities:

• Build and own production-grade, domain-oriented data products in Microsoft Fabric (Lakehouse/Delta) with documented logic, monitoring, and clear SLAs/ownership.
• Develop governed semantic models and a scalable KPI/metric system that enables consistent, trusted self-serve decision intelligence across Finance, HR, Demand, and Site Operations.
• Implement CI/CD, automated testing, and performance optimization for pipelines, models, and DAX/SQL to ensure reliability and speed at scale.
• Deliver decision-ready insights (“so what / now what”) through dashboards, alerts, and embedded workflows that accelerate decision velocity and improve operational outcomes.
• Support design and development of agentic AI solutions that automate multi-step decision workflows with governed data, guardrails, auditability, human-in-the-loop controls, and evaluation metrics.
• Lead AI and decision intelligence enablement: training, playbooks, office hours, and adoption programs; continuously measure engagement, value realization, and iterate based on feedback.
• Serve as a DI force multiplier by coaching teammates on analytics engineering, semantic modeling, and AI delivery best practices; create reusable templates and standards (“build once, use often”)
• Partner cross-functionally to align on priorities, definitions, and tradeoffs; operate with strong ownership, speed, and accountability

skills/qualifications required:

• 6+ years delivering analytics engineering or equivalent experience building production-grade data products (pipelines + semantic models + governed metrics).
• Bachelors in a technical field, masters preferred.
• Experience delivering AI-enabled analytics solutions (agents/copilots/workflow automation) with evaluation/monitoring and safe deployment patterns (human-in-the-loop, auditability).
• Demonstrated experience with CI/CD and DevOps for analytics (Git, Azure DevOps pipelines, automated testing, release processes).
• Expert SQL (T‑SQL + SparkSQL) and strong semantic modeling skills (Power BI / Tabular), including performance tuning, star schema, and metric governance.
• (Preferred) Python for orchestration, automation, testing, or ML/AI evaluation harnesses.
• Proven ability to build stakeholder trust and drive adoption through clear communication, documentation, and enablement (not just delivery).
• Ability to build training + enablement programs and measure adoption/value.
• Experience with manufacturing/operations analytics (OEE, downtime classification, yield, throughput, scrap, quality, maintenance), or adjacent industries.
• Demonstrated ability to use modern development accelerators (e.g., coding assistants/agents) responsibly to increase thro