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# Sr. Data Engineer

- **Company:** Mariana Minerals
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Databases, Continuous Integration, Data Architecture, Information Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, Reinforcement Learning, Machine Learning Operations, Industrial Software, Data Pipelines
- **Published:** October 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/sr-data-engineer-san-francisco-ca--754666ee-5ab7-4137-b8c1-ed6c8c6e46a8

## About the Role

Supply Chain, Artificial Intelligence (AI), Mining Industry Software, Automation Systems, Chemical Processes, Sales, Mining Industry, High Tech Industry, Software Design, Data Modeling, * 4+ 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.

## Description

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

Were 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, were 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, youll 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.

The Tech

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.

What You'll Do

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