Sr. Data Engineer

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.

Mariana Minerals is building the critical minerals supply chain from the ground up—and we're looking for a Senior Data Engineer to help make it autonomous.

We're not a software company selling tools to mining operators. We are a mining company that builds software. Mariana designs, builds, commissions, and operates our own mines and refineries. We develop proprietary chemical processes and run them at lab, pilot, and commercial scale. Today, we're producing battery-grade lithium salts from real oil and gas wastewater in our facilities. Our first commercial-scale lithium production facility, Lithium One, is targeting initial production in Q1 of 2027.

As a Senior Data Engineer at Mariana, you'll own a data domain end-to-end—designing the pipelines, schemas, and contracts that make a whole class of plant data trustworthy and queryable. The systems you build are the foundation every model and every operational decision depends on.

This is some of the most interesting applied data work happening today.

Our internal platform, PlantOS, uses the same reinforcement learning toolkits that power self-driving vehicles and humanoid robots—but applied to autonomous, short-interval control of mineral refining circuits. None of it works without data: every set point those models adjust, and every decision we make about a plant, rests on turning messy industrial reality into trustworthy, queryable, model-ready data.

The environment is noisy and non-stationary: sensors drift, lab results arrive late and malformed, wastewater compositions shift, equipment ages. The data backbone has to keep up. The end goal is fully autonomous refining operations—and the pipelines you build are the foundation everything else stands on.

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  Work across domains—for example, all plant sensor and historian data, or all lab and analytical results—including schema design, orchestration, reliability, and the contract it exposes to everyone downstream.

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  Design and evolve our fleet of pipelines that pull from messy industrial sources—sensors, lab systems, historians, imagery, and more—into our databases and warehouse.

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  Model time-series and analytical plant data for both human analysis and machine learning training, validation, and monitoring; own data quality, observability, and lineage in your domain.

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  Build the data architecture that feeds production ML—the training and monitoring layer—in partnership with the ML engineers who own the model-specific semantics.

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  Mentor earlier-career engineers and define the data contracts other teams build against.

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  Work the boundary with machine learning deliberately: you own the platform and the interface it exposes; ML engineers own the features and models built on top of it. The training and monitoring layer is shared ground you design together.

   

• 4-8+ years in data engineering or a closely related role.

• Strong Python and SQL, with deep experience designing database and warehouse schemas, including time-series and/or analytical data.

• Proven experience building reliable, orchestrated data pipelines and operating them in the cloud with containers and CI/CD.

• Experience with data quality, observability, and lineage, and comfort with messy real-world sources—drifting sensors, malformed exports, and the quirks of industrial systems.

• A self-starter comfortable in high-ambiguity environments, working directly with process engineers, ML engineers, and operations teams.

• Bonus: experience feeding data to ML systems—training datasets, feature pipelines, model monitoring—or working with industrial, sensor, or historian data.

   

We own the projects, generate the data, and close the loop. Every facility we build makes the software smarter—and the next facility faster and cheaper.

Mining is one of the last major industrial sectors that hasn't been rebuilt with modern software. The opportunity here isn't a feature gap—it's entire workflows and systems that don't exist yet.

Your work will directly shape how critical minerals are produced at scale in the coming decades.