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Technical Product Manager, Data & Analytics Platform

Job in Ann Arbor, Washtenaw County, Michigan, 48113, USA
Listing for: Mariana Minerals
Full Time position
Listed on 2026-09-25
Job specializations:
  • IT/Tech
    Business Systems & Technology Analysis, Data Analyst, AI Engineer (Applied/Software), Data Science Manager
Salary/Wage Range or Industry Benchmark: 140000 - 185000 USD Yearly USD 140000.00 185000.00 YEAR
Job Description & How to Apply Below

About Mariana Minerals

Mariana Minerals is a software-first, vertically integrated minerals company on a mission to supply the critical minerals powering modern energy, AI, and defense technologies. We’re reimagining the minerals supply chain by combining deep industry expertise with advanced software, automation, and data-driven decision-making.

The Role

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.

What You'll Do
  • 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.

How You'll Operate
  • 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.

What We’re Looking For

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…

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