Sr. Associate - Program Operations

Rivian and Volkswagen Group Technologies•Palo Alto, CA

18h•Hybrid

About The Position

Program Operations exists to help RV Tech execute well across every program — planning, tracking, and governing the work from concept through serial production. We are building the data, tooling, and analytics layer that execution runs on: the systems that tell leadership where capacity is going, what it costs, and where to place the next bet. You'll own and evolve the data and analytics systems that turn scattered operational signals into decision-ready insight — the platforms and models leadership uses to understand capacity, cost, and resourcing across programs, plus the financial-grade reporting that funding and planning decisions depend on. You won't just analyze these processes; you'll build and scale the tools that run them. The ideal person sits squarely at the intersection of strategy, analysis, and building. Sharp business and analytical instincts come first — the judgment to see what leadership actually needs to decide — paired with the ability to turn that into a working system. You might be a strong analytical generalist who has learned to build, or a builder who thinks in business outcomes; either way you move easily between a room with executives and the tool that serves them. The building itself can come from a traditional engineering background or from skillful use of modern AI-assisted tools — what matters is the instinct for the problem and the drive to ship something trustworthy. This role is located in our Palo Alto California office, 3 days a week onsite. This role is scoped to grow. It centers today on the core data systems and resourcing models behind program execution, with a runway to help shape how we build and run the internal tooling that helps the team execute across programs over time. We're looking for a self-starter who wants to help build something.

Requirements

Business judgment and translation. You can take an ambiguous business problem, structure it, see what leadership actually needs to decide, and communicate options and a recommendation clearly to non-technical stakeholders, including executives.
You build, not just specify. Ability to take an idea and produce a working tool or prototype yourself — through hands-on coding (e.g., Python/SQL, scripting, APIs) and/or skillful use of AI-assisted development tools to build, test, and iterate. What matters is shipping something that works and can be maintained.
Self-direction. A self-starter who can take a goal and run independently — figuring out the path, unblocking yourself, and delivering without a detailed playbook.
Sharp analytical instincts. Strong quantitative and problem-structuring skills; comfort with financial/operational data and precision where funding and auditability are on the line.
Strong-fit background We care more about instincts and what you can build than about how you learned to build it. Strong fits include a sharp analytical generalist — for example, a strong MBA or operator — who has the instinct for these problems and builds with modern AI-assisted tools; or a developer or technically-trained person who moved into a business, financial, or data analyst role. Either way, you think in terms of cost, capacity, and decisions, and you're hands-on enough to build.

Nice To Haves

Experience with BI/analytics tooling (e.g., Hex) and workflow platforms (e.g., Slack apps, internal APIs).
Exposure to FP&A, resourcing/capacity planning, or cost and contract estimation concepts.
Background in Automotive, Hardware, or Software Development environments.
Bachelor's degree in a quantitative, engineering, or business field — or equivalent demonstrated capability.
3 to 4 years of relevant experience is typical, but we weigh skills and what you've built over years-in-seat.

Responsibilities

Develop the Business Model capacity and cost.
Drive headcount modeling, resourcing scenarios, and estimation that leadership uses to decide whether teams can absorb new work.
Produce decision-ready insight. Build reporting that surfaces anomalies, completion, and distributions — so leaders manage exceptions, not blanket reviews.
Translate for executives. Prepare clear analyses, memos, and updates that turn data into a recommendation with a point of view.
Build and operate Own platforms, not just reports. Maintain, improve, and extend the data systems and internal tooling the team relies on — data models, validation logic, workflow automation, and clean downstream feeds to Finance.
Turn requirements into shipped tools. Take an ambiguous business need, scope an MVP, build it, and iterate it into something reliable enough to depend on.
Automate the manual. Replace spreadsheet-and-glue processes with maintainable code, pipelines, and integrations (e.g., Slack, internal APIs, BI tools such as Hex).