Technical Product Manager, Data & Analytics Platform at Mariana Minerals in San Francisco HQ

Job description

• Overview
• Mariana Minerals is a software-first, vertically integrated minerals company supplying the minerals critical to modern energy, AI, and defense technologies. Our data systems don't live in a vacuum — they feed process simulators, drive site automation, and are the substrate the business runs its decisions on.
• We're hiring a Technical Product Manager to be the single product owner for the data engineering family — the data platform, analytics, data science, and the AI and agentic workflows built on top of them. Today this work is spread across technical leads and absorbed informally, with no one setting direction across the whole. Your job is to change that: decide what the data organization builds and why, get stakeholders aligned behind it, and make sure engineers can stay heads-down on the work that matters.
• This is a hybrid Pioneer/Settler role: you'll get lightweight demos and PoCs built on ambiguous problems, and you'll bring order and cohesive ownership to data work streams that already exist. You'll prioritize ruthlessly and visibly — being explicit about what will not be worked on — and set direction rather than waiting for it to be handed to you.
• Most data PM roles hand you a mature warehouse and a dashboard backlog. This role hands you the data platform and analytics layer the business runs on, plus the AI workflows that will increasingly run on top of it, at the moment they move from ad hoc scripts and informal ownership into supported software. At Mariana, you don't need to validate product-market fit, because we are the market: if you identify a real problem and build a real solution, adoption is a conversation, not a sales cycle.

Responsibilities

• Talk to our internal teams constantly — operators, machine learning engineers, software developers, analysts, and data users are your users, and their problems set the roadmap.
• Own the roadmap for the data platform, analytics, and data science tooling: what gets built next, for whom, and why.
• Write the product specs, KPIs, and success metrics that turn ambiguous asks into scoped, shippable work.
• Own prioritization and stakeholder alignment so engineers stay focused on deep technical work.
• Make the build-vs-buy calls for BI and analytics, and prioritize tooling that lets stakeholders answer their own questions instead of adding headcount.
• Own where agents fit versus conventional software, and drive AI and agentic workflows from demo to adopted tool.
• Decide which new data streams get integrated, when, and to what standard as new sensors and sources come online.
• Set the bar for what "supported" and "correct" mean as tools graduate from scripts and spreadsheets, and be explicit about what won't be migrated.
• Structure from ambiguity: Bring direction to cross-disciplinary initiatives and align stakeholders rather than waiting for direction.
• Ruthless, visible prioritization: Be explicit about what will not be worked on, not just what will.
• Hybrid Pioneer/Settler: Get initial prototypes built with stakeholders, then write the roadmap to validate the use case and, if worth it, take it to production.
• Ecosystem fluency: Understand where Mariana's data sits relative to the systems that consume it and relative to the current frontier of LLM capability, so prioritization calls stay well-calibrated.

Requirements

• Must have
• 4–8+ years in technical product management, data platform or analytics product roles, or equivalent experience leading cross-disciplinary technical programs
• Comfortable being the only PM in a highly technical room — you know when to drive a decision, when to defer to engineers, and how to build credibility without being the most technical person there
• Enough depth in data infrastructure and analytics to earn credibility with data engineers, data scientists, and analysts, and to know when a technical answer is a good one
• Working fluency with the current LLM/foundation-model landscape and where agentic approaches genuinely fit versus conventional software
• Track record of bringing structure to ambiguous, cross-functional initiatives: setting direction, aligning stakeholders, and owning outcomes end-to-end
• Hands-on prototyping ability — you can get a PoC built with stakeholders, not just write requirements for one
• Strong prioritization and tradeoff skills — you know when to say no, and you say it visibly
• Exceptional written and verbal communication across audiences, from data engineers to operators to executives
• Product ownership of a data platform, warehouse, or analytics stack in production
• Experience with BI/analytics tooling strategy (Hex, Looker, or embedded analytics)
• Experience shipping LLM-powered or agentic internal tools
• Background in mining, energy, chemicals, manufacturing, or other heavy industry
• Experience leading the transition from spreadsheets and scripts to supported software

Skills

Required

• Technical product management
• Data infrastructure and analytics depth
• LLM/foundation-model landscape fluency
• Hands-on prototyping ability