Senior Business Analytics Specialist

What Success Looks Like In this role, you will: Enable trusted data products that power analytics, automation, and AI experiences across MCAPS and Finance. Improve platform scalability, reliability, governance, and performance for over 50,000 monthly users. Enable secure downstream consumption of data products and self-serve analytics - delivering governed, performant semantic models that let business users build their own analytical and AI solutions while preserving performance, security, and compliance. Build AI agents and AI solutions that automate repetitive, tedious data engineering and analytical tasks - freeing engineers to lead with intent-first design and accelerating the journey to AI-native data engineering. Processing & compute: Apache Spark (PySpark, Spark SQL), Fabric Notebooks, Dataflows Gen2, Data Factory pipelines, and T-SQL. Build and maintain scalable Lakehouse, Warehouse, and Direct Lake semantic model solutions that serve analytics and AI consumption at scale. Contribute to the adoption of intelligent automation and AI-powered engineering across the platform, treating AI as a first-class engineering actor rather than an occasional tool. Build and optimize data models that power enterprise reporting, analytics, and self-service BI experiences. Enable trusted, high-quality datasets that support analytics, automation, and AI workloads. Enable secure downstream consumption of data products: exposing curated, governed datasets to reports, automation, and AI solutions through well-defined contracts, access controls, and sensitivity labeling, so consumers get trusted data without compromising security or compliance. Collaborate with BI Leads to design scalable semantic models that support enterprise-grade reporting and self-service analytics. Implement data modeling best practices that improve performance, discoverability, usability, and AI readiness. Contribute to the definition and enforcement of modeling standards, reusable design patterns, and semantic-layer governance. Enable semantic models that power self-serve analytical scenarios and self-serve AI solutions for business users - delivering governed, performant, and AI-ready models that let users explore data and build their own insights while preserving query performance, security, and compliance. Investigate and resolve data quality, performance, refresh, pipeline, and platform-related issues. Manage Fabric capacity and Compute Unit (CU) utilization - diagnosing throttling, analyzing query plans, and right-sizing workloads for predictable cost and performance. Implement secure-by-design engineering practices throughout the data platform lifecycle. Ensure solutions meet organizational standards for privacy, security, Responsible AI, and regulatory compliance. Contribute reusable governance patterns that enable scalable self-service analytics while protecting sensitive data assets. Master's Degree in Mathematics, Analytics, Engineering, Computer Science, Marketing, Business, Economics or related field AND 3+ years experience in data analysis and reporting, business intelligence, or business and financial analysis OR Bachelor's Degree in Statistics, Finance, Mathematics, Analytics, Engineering, Computer Science, Marketing, Business, Economics or related field AND 4+ years experience in data analysis and reporting, business intelligence, or business and financial analysis OR equivalent experience. Experience using AI-native engineering tools such as GitHub Copilot, Copilot for Fabric, Azure OpenAI, or similar technologies. Experience building AI-ready data architectures and preparing trusted data assets for AI consumption. Understanding of AI agents, vector search, and semantic search, and modern AI application patterns, including prompt engineering, evaluation, and guardrails. Experience leveraging AI to accelerate software development, testing, documentation, code review, performance optimization, and operational efficiency. Familiarity with Responsible AI principles, AI governance, and the secure use of generative AI technologies. Experience integrating AI capabilities into analytics, reporting, automation, or business-process solutions. Experience supporting enterprise Power BI solutions, DAX optimization, and semantic modeling best practices. Experience implementing data governance and metadata management solutions. Experience working on customer-facing platforms or large-scale enterprise analytics ecosystems. Knowledge of streaming, real-time analytics, event-driven architectures, and operational intelligence scenarios. Experience supporting mission-critical platforms with high availability, reliability, and performance requirements. GitHub Certified: GitHub Copilot (GH-300)