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Microsoft· Spotted 5h ago

Senior Technical Program Manager - FD&E

Location
Redmond, WA, US
Level
Senior
Experience
4+ years
Education
Bachelor's degree
Job function
Software/Internet/AI › Project Management › Technical Project Manager

Posted Nov 10, 2026Last confirmed open Nov 11, 2026

About this role

Overview Do you want to help Microsoft understand the business behind the world’s leading developer platform and AI coding tools? Do you want to turn complex revenue, consumption, and cost data from CoreAI, GitHub and Microsoft Developer Tools into trusted, AI-ready data products that finance leaders rely on to close the books, forecast the business, and make investment decisions?

Do you want to be a hands-on builder who helps finance teams put AI to work and shapes how Finance builds on a secure, scalable data and AI platform?

If so, we have a great opportunity for you in the Finance Data & Experiences (FD&E) global organization. In FD&E, we build trusted, connected, and compliant solutions at scale that enable Microsoft to run its business more efficiently and effectively. We operate within the business and have robust delivery partnerships across Engineering Groups, Finance, Sales, and Marketing.

Our products influence data-driven decision-making for audiences ranging from the company’s Senior Leadership Team to finance analysts and business partners. We pride ourselves on a culture of customer centricity, innovation, agility, transparency, and flexibility.

As a Senior Technical Program Manager , you will be the FD&E product and program lead for the CoreAI, GitHub and Developer Tools Finance team.

You will be empowered as a builder: guiding and supporting Finance stakeholders in adopting AI tools, building proofs of concept that guide engineering toward scalable AI solutions, and establishing the engineering framework that enables Finance to build edge solutions and tools on a secure, scalable Finance data and AI platform.

You will own the roadmap for the data and analytics solutions that support GitHub billed and net revenue reporting, monthly close, forecast and budget, and consumption-based offerings such as GitHub Copilot.

You will partner closely with finance and business stakeholders to understand their decision-making needs, translate ambiguous and often urgent requests into clear business definitions, data contracts, and technical requirements, and work with engineering teams to deliver accurate, scalable, secure, and sustainable solutions.

This role connects deep finance acumen with technical program leadership across source systems, semantic models and cubes, planning and forecasting platforms, identity and customer mapping, data governance, and AI-enabled reporting experiences — and it will accelerate your growth in data platforms, AI, and the economics of a fast-growing, consumption-driven business.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more, and we’re dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their best each day. Join us and help shape the future of the world.

Responsibilities

  • Own the product vision, program goals, roadmap, and prioritized backlog for GitHub and Developer Tools finance data solutions, including the GitHub Revenue semantic model, revenue and close reporting, forecast and budget data, and consumption and usage analytics. Use business impact, data quality, capacity, dependencies, and long-term sustainability to make and clearly communicate tradeoffs.
  • Serve as the primary FD&E partner to GitHub and Developer Tools Finance and adjacent stakeholders across Corporate Finance, GitHub, Engineering Groups, MCAPS, and platform teams. Build trusted relationships and lead alignment on definitions, scope, ownership boundaries, milestones, and release decisions for complex cross-organizational programs.
  • Translate broad finance questions into detailed requirements, metric definitions, data investigations, data contracts, user stories, acceptance criteria, and test scenarios. Drive solutions for hard data problems such as integrating more granular forecast and budget data into close reporting, reconciling GitHub and Microsoft data, and mapping GitHub entities and identities to Microsoft customer and organizational hierarchies.
  • Drive end-to-end execution through backlog grooming, sprint planning, dependency management, risk mitigation, stakeholder communications, user acceptance testing, deployment readiness, launch, and continuous improvement. Establish clear operating rhythms and hold contributors accountable for commitments, quality, and timely escalation.
  • Assess and shape incoming requests, including high-visibility, time-sensitive asks from senior leadership. Size the work, surface implications for scope, capacity, governance, privacy, and production support, propose phased paths (interim solution and durable target state), and bring leadership the clear decisions needed before committing.
  • Champion data quality, Finance reconciliation, privacy, security, and responsible AI. Define access controls for sensitive data, monitor data completeness and mapping gaps, establish production support models (ownership, SLAs, incident and escalation paths), and partner with engineering to improve the performance and maintainability of the solutions.
  • Act as a hands-on AI builder and coach for Finance. Guide and support GitHub and Developer Tools Finance stakeholders in adopting AI tools such as Microsoft 365 Copilot, GitHub Copilot, and agents; identify high-value use cases; share prompts, patterns, and best practices; and help finance users build their own productivity solutions safely and effectively.
  • Build proofs of concept and prototypes, including AI agents, automated close and reporting workflows, and AI-ready semantic models, that validate business value and feasibility quickly. Use them to define requirements, architecture direction, and evaluation criteria that guide engineering in building scalable, production-grade AI solutions.
  • Initiate and drive the engineering framework and operating model that enables Finance to build edge solutions and tools on a secure, scalable Finance data and AI platform. Define the guardrails, reusable components, environments, governance, and support model that distinguish finance-built solutions from engineering-owned products, and set clear criteria for when a solution graduates to engineering ownership.
  • Act as a technical and business advisor: explain complex measures and data behavior in clear language, lead demos and training, drive adoption, manage solution transitions and deprecations, and use customer feedback to refine the roadmap.
  • Act as customer zero for advanced AI products and features and partner with Frontier Finance to shape product priorities and upgrades around Finance use cases.

Required Qualifications

  • Bachelor's Degree AND 4+ years experience in engineering, product/technical program management, data analysis, or product development OR equivalent experience.
  • 2+ years of experience managing cross-functional and/or cross-team projects.

Preferred Qualifications

  • Bachelor's Degree AND 8+ years experience engineering, product/technical program management, data analysis, or product development OR equivalent experience.
  • 6+ years of experience managing cross-functional and/or cross-team projects.
  • 1+ year(s) of experience reading and/or writing code (e.g., sample documentation, product demos).
  • Experience defining and delivering enterprise finance data, business intelligence, or AI-enabled analytics products, including semantic models or cubes, data contracts, reconciliation rules, user acceptance testing, and launch and production readiness.
  • Experience with revenue reporting, monthly close, forecasting and budgeting, subscription or consumption-based (usage/token) business models, or cost and spend analytics; familiarity with GitHub or developer tools businesses is a plus.
  • Familiarity with SQL, Power BI, Azure data services (e.g., Azure Data Lake, Fabric), multidimensional or tabular models, generative AI, or comparable data and analytics technologies.
  • Hands-on

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