Mariana Minerals is looking to build the semantic layer between process engineering and AI-driven operations by owning the data platform that connects industrial systems to ML models.
Requirements
- 5+ years of Python development experience, focused on data engineering and modeling.
- Proven experience creating semantic layers and analytics models bridging technical and business contexts.
- Strong software engineering fundamentals—version control, testing, documentation, and maintainable code.
- Hands-on experience with modern data tools (Airflow/Dagster, dbt, data quality frameworks).
- API integration experience (REST, OAuth, rate limiting, retries).
- Familiarity with time-series or industrial data (PLCs, LIMS, SCADA).
- Exposure to ML pipelines and real-time data processing.
Responsibilities
- Design, build, and maintain data pipelines in Python that integrate with our digital twin simulator, ML infrastructure, and operational systems.
- Develop semantic data models and analytics layers that bridge process engineering, ML model requirements, and business metrics.
- Architect data systems that support both real-time operations and historical analysis, prioritizing semantic accuracy and data quality over massive scale.
- Evolve our data platform as we scale from our first facility to multiple operations—defining the foundations of a modern, industrial data stack.
- Partner with ML engineers and process experts to ensure data is clean, consistent, and fit for model training and inference.
- Build and maintain API integrations with enterprise and industrial systems (PLCs, LIMS, historians, engineering tools).
- Support real-time data exchange between control systems, dashboards, and analytics platforms.
Other
- Clear communication and collaboration skills; ability to work with ML, software, and operations teams.
- A structured yet creative problem-solving mindset and comfort with ambiguity.
- Experience designing data systems in manufacturing, energy, or process industries.
- Join a culture that values Extreme Ownership, Engineering Out Requirements, then Automating, and Sharing Your Legos.
