Internal Tools & Data Product Analyst

Lambda•San Francisco, CA

8d•$150,000 - $200,000•Hybrid

About The Position

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU. If you'd like to build the world's best AI cloud, join us. Note: This position requires presence in our San Francisco office location 4 days per week; Lambda’s designated work from home day is currently Tuesday. As companies scale, the gap between what leadership needs to see and what teams can easily report grows — dashboards get built by hand, workflows stay stuck in spreadsheets, and requests for better tooling pile up faster than they get solved. This role exists to close that gap: turning manual reporting into trusted, self-serve data products, finding and fixing the workflows costing teams the most time, and helping make sure the highest-impact tooling requests actually get built. About This Role The Internal Tools & Data Product Analyst partners with business teams to find where manual work, inconsistent data, or missing tooling is slowing things down, and helps turn that into a shipped fix. This might mean defining what a metric actually means and getting a dashboard built around it, helping retire a spreadsheet-driven process that shouldn't exist anymore, or helping triage and prioritize a backlog of tool requests. Some of this gets shipped directly — lightweight builds, AI-assisted coding — and the rest gets scoped into a clear spec and handed to an engineering partner.

Requirements

Early-career product, business operations, analytics, or technical program management experience
Comfort working directly with data, spreadsheets, and lightweight tooling (SQL, no-code platforms, AI coding assistants)
Some exposure to designing a workflow or tool from the end user's point of view, not just the data behind it
A track record of shipping something — a tool, a process fix, an internal script — rather than only recommending one
End-user experience judgment: can tell the difference between a technically correct dashboard/tool and one people will actually use
Prioritization: can weigh impact against effort across a noisy backlog
Lightweight execution: comfortable using AI-assisted coding tools to ship small things directly
Cross-functional relationship building
Clear written communication (specs, roadmaps, requirements)

Responsibilities

Partner with business stakeholders to help define what key metrics actually mean, and support getting agreement on a single trusted source for each one.
Contribute to requirements for turning manually-built, inconsistent reporting into live, self-serve dashboards suitable for execs — including how someone drills from a summary number into the underlying detail.
Help prioritize which reports or metrics get automated first, based on how manual, stale, or error-prone they are today.
Identify manual, spreadsheet-driven work worth automating, and help see the fix through end to end.
Scope each fix and help decide what to build directly (lightweight scripting, no-code tools, AI-assisted coding) versus what needs a full engineering build.
Write clear, engineering-ready specs for anything handed off to a technical partner.
Identify and document places where an AI agent or automation could replace manual coordination, feeding the broader automation roadmap.
Support the tool-request pipeline: help collect requests, triage them, and prioritize based on impact versus effort.
Help maintain a visible roadmap so stakeholders understand what's coming next and why.
Build relationships with the teams you support so priorities stay grounded in what they actually need.