Data Engineer Co-op

Found Industries is a Cambridge, MA-based industrial technology company building new critical supply chains from resources and infrastructure hiding in plain sight. Launched out of MIT, Found develops and deploys technologies that unlock critical materials and dispatchable clean energy from overlooked industrial feed stocks.

The company operates through two complementary business units:
Found Metals and Found Energy. Found Metals recovers strategic materials, including gallium, from existing industrial processing streams. Found Energy converts aluminum, including low-grade and scrap material, into a fuel for behind-the-meter industrial heat and power. The two businesses reinforce one another:
Found Metals expands domestic supplies of critical materials used in advanced energy technologies, while Found Energy creates new applications and demand for those materials. Together, they form the foundation of a broader industrial platform focused on strengthening U.S. energy and materials security.

At Found Industries, we are building the industrial capabilities needed for a more resilient and self-reliant future. We believe secure access to critical materials and energy is foundational to economic strength, national security, and long-term prosperity, and we are looking for talented people motivated by that mission to join us. For more, visit  .

As a Data Engineer Co-op
, you will be instrumental in building and maintaining the data infrastructure necessary to analyze and improve our pilot systems. You will work closely with our R&D, automation, and engineering teams to collect, organize, and analyze data generated by our systems. You will also collaborate closely with our chemistry and materials science teams to support experiment tracking, characterization workflows, and data management for lab-based R&D.

Your work will help drive efficiency, reliability, and scalability in our operations, playing a key role in advancing our mission to create impactful climate technologies.

Co-op Duration: 6 months, Jan
- June (earlier start available/encouraged, extensions may be available)

• Design, build, and maintain scalable data pipelines to collect and process real-time and historical data from pilot systems and lab systems.
• Work directly and cross functionally with materials science, chemical engineering, and mechanical engineering departments to determine workflows and build tools for experiment tracking, sample management, and characterization data ingestion.
• Develop and optimize database schemas and storage solutions for structured and unstructured data.
• Integrate data from diverse sources, including sensors, control systems, and third-party APIs, ensuring data quality and consistency.
• Collaborate with automation and R&D teams to define data requirements for system monitoring and performance analysis.
• Create dashboards and visualizations to monitor system performance, enabling data-driven decision-making.
• Develop and implement algorithms for data analysis, including trend detection, anomaly identification, and predictive modeling.
• Ensure data security, integrity, and compliance with company and industry standards.
• Participate in design reviews and contribute to the development of best practices for data engineering processes.
• Document data workflows, pipelines, and technical procedures.

• Passionate about climate tech and its potential to impact the world.
• Pursuing a degree in Computer Science, Data Engineering, Software Engineering, Applied Math, or a related field.
• Familiarity with programming languages such as Python, Type Script, and SQL
• Experience with data processing frameworks such as Pandas, Apache Spark, or similar.
• Knowledge of relational databases (e.g., PostgreSQL, MySQL)
• Familiarity with cloud platforms (e.g., AWS, Azure, or Google Cloud) and their data engineering tools.
• Understanding of data visualization tools (e.g.,Grafana, Tableau, Power BI, or Matplotlib).
• Experience with version control systems like Git and collaborative workflows.
• Ability to understand scientific data structures, such as experiment logs, sample metadata, or characterization results.
• Ability to work in a multidisciplinary environment, collaborating with hardware, software, and chemical engineers.
• Strong analytical, problem-solving, and communication skills.

• Experience with time-series databases (e.g., InfluxDB, Timescale DB).
• Experience working in a scientific or research lab environment
• Exposure to…