Senior Business Analytics Specialist
Data Science
Redmond, WA, USA
USD 106,400-203,600 / year
The BI COE (Business Intelligence Center of Excellence) Platform team designs, engineers, and operates the AMP Data Platform - a centralized, enterprise-scale data platform powering business intelligence, analytics, process automation, and AI solutions across Microsoft.
Built and operated natively on Microsoft Fabric, the platform is the foundation for more than 120 semantic models, 200+ reports, numerous automation solutions, and emerging AI experiences that support over 50,000 monthly users across the Microsoft Customer and Partner Solutions (MCAPS) and Finance organizations.
As one of Microsoft’s largest Fabric enterprise implementations, the platform also serves as a Customer Zero environment, partnering closely with the Microsoft Fabric Product Group to validate, influence, and scale next-generation platform capabilities.
We are on a journey to reinvent data engineering and our data platform for the age of AI — building an AI-native data platform where intelligent agents take on much of the repetitive, time-consuming data engineering work, and engineers lead with intent-first engineering: defining direction, specifications, and judgment while agents help build, test, and validate. This role is central to that shift.
We are seeking a technical Senior Business Analytics Specialist to help build and operate the next generation of Microsoft’s analytics and AI platform. The successful candidate combines strong data engineering fundamentals with deep expertise in modern cloud analytics platforms, AI-native engineering practices, and enterprise-scale governance. You will work alongside Data Architect, Data engineers, BI Leads, data product owners, and product engineering teams to deliver secure, scalable, AI-ready data solutions that accelerate business impact. This is a hands-on IC role. Every system and solution we build is secure by design - security and privacy are part of our system design from the start, not an afterthought, and AI-native engineering is core to how the team works.
Responsibilities
What Success Looks Like
In this role, you will:
- Help operate one of Microsoft’s largest Microsoft Fabric enterprise platforms.
- Enable trusted data products that power analytics, automation, and AI experiences across MCAPS and Finance.
- Accelerate Microsoft’s transition toward AI-native engineering through practical adoption of AI-native development practices.
- Improve platform scalability, reliability, governance, and performance for over 50,000 monthly users.
- Partner with Microsoft Fabric Product Group teams to influence and validate next-generation platform capabilities through Customer Zero adoption.
- 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.
Our Technology Environment
You will work hands-on across a modern, Fabric-native stack:
- Lakehouse & storage: Microsoft Fabric, OneLake, Delta Lake / Parquet, Lakehouse & Warehouse, shortcuts, and medallion (bronze / silver / gold) architecture.
- Processing & compute: Apache Spark (PySpark, Spark SQL), Fabric Notebooks, Dataflows Gen2, Data Factory pipelines, and T-SQL.
- Semantic & BI: Power BI, Direct Lake, Direct Query and Import mode semantic models, DAX, the VertiPaq engine, and incremental refresh.
- Real-time: Real-Time Intelligence, Eventstreams, and Eventhouse / KQL databases.
- AI & agents: GitHub Copilot, Copilot for Fabric, Microsoft Agency, Azure OpenAI / Azure AI Foundry, RAG, vector search, and agent frameworks.
- Governance & security: Microsoft Purview, sensitivity labels, data lineage, RBAC, and Microsoft Entra ID.
- Engineering & DataOps: Git, Fabric deployment pipelines, CI/CD (Azure DevOps / GitHub), automated data-quality testing, and observability.
- Languages: Python, SQL / T-SQL, PySpark, DAX, and KQL.
AI-Native Data Platform Engineering
- Design, develop, and optimize enterprise-scale data pipelines, data products, and platform services on Microsoft Fabric.
- Build and maintain scalable Lakehouse, Warehouse, and Direct Lake semantic model solutions that serve analytics and AI consumption at scale.
- Use AI-native development tools and agentic workflows to accelerate delivery, testing, documentation, and operational excellence - working in an intent-first, spec-driven model where engineers define intent and approved agents help execute and validate.
- 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.
Data Engineering & Analytics Enablement
- Engineer robust, idempotent ELT/ETL pipelines in PySpark, Spark SQL, and T-SQL across Fabric Notebooks, Dataflows Gen2, and Data Factory pipelines, applying medallion (bronze / silver / gold) patterns over Delta Lake.
- Tune Spark and Delta workloads for scale: partitioning, shuffle and data-skew management, broadcast joins, caching, Adaptive Query Execution, and file optimization (OPTIMIZE / V-Order, compaction).
- Build and optimize data models that power enterprise reporting, analytics, and self-service BI experiences.
- Partner with BI Leads and business stakeholders to translate complex business requirements into scalable technical solutions.
- Enable trusted, high-quality datasets that support analytics, automation, and AI workloads.
- Onboard and modernize new analytics workloads and data products directly onto the Fabric-native platform.
- 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.
Semantic Modeling & Enterprise BI
- 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.
- Optimize DAX, Direct Lake / import model performance, VertiPaq compression, aggregations, and incremental refresh for high-concurrency reporting workloads.
- 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.
Platform Reliability & Operations
- Monitor, troubleshoot, and optimize production workloads supporting 50,000+ users and mission-critical business processes.
- 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.
- Drive continuous improvements in observability, monitoring, DataOps, DevOps, CI/CD, and platform automation.
- Partner with the Microsoft Fabric Product Group to evaluate new capabilities and enterprise-scale deployment patterns through Customer Zero adoption.
Security, Compliance & Governance
- Implement secure-by-design engineering practices throughout the data platform lifecycle.
- Support enterprise governance - data classification, lineage, access control, auditing, and compliance using Microsoft Purview, One lake Catalog or similar products.
- 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.
Qualifications
Required/minimum qualifications
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.
Preferred Qualifications
- 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 across Microsoft Fabric workloads - Lakehouse, Warehouse, Semantic Models, OneLake, Data Factory, and Real-Time Intelligence.
- Experience supporting enterprise Power BI solutions, DAX optimization, and semantic modeling best practices.
- Experience implementing data governance and metadata management solutions.
- Hands-on experience with Microsoft Purview - cataloging, lineage, classification, and information protection.
- 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.
- Microsoft Certified: Fabric Data / Analytics Engineer Associate (DP-600 / DP-700)
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- GitHub Certified: GitHub Copilot (GH-300)
- Microsoft Certified: Azure Data Engineer / Azure ML Associate (DP-100)
- Microsoft Certified: Security, Compliance, and Identity Fundamentals (SC-900) or Information Protection & Compliance (SC-400)
#CEAIJobs
Business Analytics IC4 - The typical base pay range for this role across the U.S. is USD $106,400 - $203,600 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $137,600 - $222,600 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.