Technical Program Manager, Analytics & GenAI
Amazon
Technical Program Manager, Analytics & GenAI
Amazon
Sells products online and rents cloud computing
Are you a builder who takes a messy business process and, in days not quarters, turns it into a working data-and-AI solution people actually adopt? Do you pair that with the technical depth to design the solution well, raise the quality bar on what gets shipped, and lift the builders around you?
As Technical Program Manager, Analytics & GenAI within Data Center Procurement Governance (DCPG), you will be a hands-on, technically deep builder-enabler who takes generative-AI and analytics capabilities end to end: from the business process and the build decision, through the solution and the data it stands on, to the enablement and adoption that make it stick. This is a build-and-own role, not a coordinate-and-hand-off role.
You bring a higher technical bar than a typical program manager - strong enough in data and software practice to build fit-for-purpose solutions quickly, review the quality and security of what the team builds, and mentor other builders. You are a program manager first: you own the problem, the build decision, and the outcome and you have the technical depth to deliver it yourself and raise the bar for everyone around you.
This role works across a broad array of procurement practitioners and partner teams in a global, matrixed organization. The ability to make an impact and drive outcomes amid an ambiguous, complex landscape is critical. Due to the global nature of the role, flexibility across time zones is needed.
Your role includes:
- Take a business process, understand it deeply, and iterate quickly to generate solutions - re-engineering the workflow and folding its logic into the build rather than automating it as-is.
- Build generative-AI, analytics, and automation solutions end to end on modern low-code and GenAI platforms - fit-for-purpose, capability-grade solutions you own through iteration, built close to the business process.
- Build and own analytics and insight - dashboards, metrics, and self-updating mechanisms that surface signal proactively to decision-makers.
- Be data-strong: design and stand behind the data foundation your solutions rely on so analytics and GenAI output can be trusted, not just believed.
- Provide technical leadership and mentorship, review the quality and security of what the team builds (including code and application-security review), uphold engineering and data-integrity standards, and grow the capability of the builders around you.
- Partner with specialist engineering teams on heavier, full-stack platform builds - scoping and handing off work that belongs there, while owning rapid, process-proximate building yourself.
- Change management & adoption (owned, not an afterthought), own landing and adoption for what you deliver: enablement, training, feedback loops, and iteration until the solution is the way the work gets done. A build that doesn't land hasn't delivered; you are accountable for adoption outcomes, not just shipped tools.
Key job responsibilities
1. Translate business processes into solutions — owning the 'what to build and why' decision and re-engineering workflows where that delivers more value than automating as-is.
2. Design, build, and iterate GenAI, analytics, and automation solutions end to end, taking them from idea to adopted state.
3. Build analytics, dashboards, and reporting mechanisms that measure outcomes and surface insight proactively.
4. Design and stand behind the data foundation (sources, validation, provenance) that keeps analytics and GenAI trustworthy.
5. Provide technical leadership: review the quality and security of solutions the team ships (including code and application-security review) and mentor other builders.
6. Partner with specialist engineering teams on full-stack/platform builds, scoping clear boundaries and handoffs.
7. Drive change management and adoption to measurable targets across multiple stakeholder groups and regions.
8. Operate independently in ambiguity, building fit-for-purpose with incomplete or evolving requirements.
A day in the life
- No standard day — global scope and much variety: a morning sync with US team members, a working session with APAC, an EMEA call to close.
- Real time spent building — designing a solution, shipping a GenAI tool, or reviewing and uplifting a teammate's build — not only coordinating.
- Your customers are the data center procurement teams and partner teams; stakeholders include legal, finance, assurance, and security colleagues.
- You are both the builder who ships and the technical mentor the team relies on for review and quality.
About the team
Data Center Procurement Governance (DCPG) is a global function that enables Amazon's data center procurement organization to execute at scale — building the operational backbone of programs, mechanisms, systems and tooling, analytics, and generative AI that lets the procurement organization move faster. This role is the analytics and GenAI build-and-enable capability at the center of that work, partnering with procurement to build and iterate, whilst working with specialist engineering teams for heavier platform development and scaling.
Inclusive Team Culture
AWS values curiosity and connection. Our department, Data Center Planning and Delivery (DCPD), sponsors a Women's Leadership Program that supports the growth and development of female builders across the organization. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events build stronger, more collaborative teams. Our continual innovation draws on the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
- 4+ years in technical program management, building or delivering technical solutions hands-on (not only coordinating engineering teams).
- Experience developing and deploying GenAI, analytics, or automation solutions to production and iterating them.
- Strong data capability — designing and working with data models, validation, and the data foundation solutions rely on (e.g. SQL and a general-purpose/scripting language such as Python).
- Working knowledge of technology risk, data/security, and compliance considerations — builds to security and quality standards and shepherds solutions through review.
- Experience driving change management and adoption of technical solutions to measurable outcomes.
- Experience with automation or scripting (Python or equivalent) and building analytics, BI, or dashboards.
- Experience mentoring or raising the technical bar for other builders.
- Track record of designing data validation, guardrails, and lineage into solutions that consume untrusted or migrated data.
- Experience influencing stakeholders across functions, geographies, or business units.
- Domain exposure to procurement, supply chain, or data center supply/delivery.
- Experience architecting or operating solutions on AWS.
- Experience defining outcome metrics (adoption, time saved, quality) and presenting recommendations to leadership.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Amazon runs the largest online store in the United States and rents cloud computing to businesses and governments through Amazon Web Services. AWS pioneered renting computing power over the internet, and Amazon built its own delivery network to get orders to customers quickly. Shoppers buy from Amazon and from independent sellers on its store, and Prime members pay a subscription for fast free shipping, streaming video and music, and other perks. AWS rents out computing power, storage, databases, and AI tools so companies can run software without owning servers, and Amazon also makes Kindle e-readers and Echo speakers with the Alexa assistant. Jeff Bezos founded Amazon in 1994, and it is based in Seattle, Washington.
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