How to apply for Engineering Manager, Data Systems

About KoBold Metals

KoBold Metals is revolutionizing mineral exploration by combining AI models with novel sensors to discover critical metals for electrification. Unlike traditional mining companies, they're both the largest independent mineral exploration company and the largest exploration technology developer, with a proven track record of discovering high-quality copper deposits using significantly less capital than industry averages. Working here means directly contributing to climate change solutions by enabling the transition to electric vehicles and renewable energy through smarter resource discovery.

About the role

As Engineering Manager for Data Systems, you'll lead the team responsible for the data infrastructure that powers KoBold's AI-driven mineral exploration platform. This role involves managing systems that process geological, sensor, and exploration data from over 60 projects worldwide, directly impacting the company's ability to discover critical metals more efficiently. Your work will enable data scientists and geologists to make better decisions about where to explore, accelerating the discovery of resources needed for electrification.

A typical day

You might start your day reviewing data pipeline performance from overnight sensor data processing, then meet with exploration geologists to understand their data needs for an upcoming field program in Canada. In the afternoon, you'd work with your engineering team on scaling the data infrastructure to handle new types of geological imagery, followed by planning sessions with data scientists about upcoming model training requirements. Throughout the day, you're balancing technical leadership with understanding how your systems directly impact exploration decisions across KoBold's global portfolio.

Who KoBold Metals is looking for

• Has experience managing data engineering teams in environments where data quality directly impacts business outcomes (like scientific research, geospatial analysis, or resource discovery)
• Understands how to build and scale data systems that support both real-time sensor data and large historical geological datasets
• Can bridge the gap between technical teams (data scientists, software engineers) and domain experts (exploration geologists)
• Has worked in companies that combine technology development with physical operations, understanding the unique challenges of field data collection and processing

Tips for this application

• Highlight specific experience with geospatial data, sensor data processing, or scientific data systems - not just generic data engineering
• Demonstrate understanding of how data infrastructure enables AI/ML models, particularly in resource discovery or similar domains
• Show examples of managing teams that work closely with domain experts (like scientists or field researchers)
• Reference KoBold's specific achievements, like their copper discovery or portfolio of 60+ projects, to show you've done your homework
• Emphasize experience with systems that handle both structured geological data and unstructured sensor/field data

What to cover in your cover letter

['Your experience managing data systems that directly enable scientific discovery or resource optimization', "How you've successfully led teams that bridge technical and domain expertise (like data engineers working with scientists)", "Your understanding of the mining/exploration industry's data challenges and how technology can solve them", "Why you're specifically excited about KoBold's mission to use AI for climate-positive mineral discovery"]

Research before applying

• KoBold's specific copper discovery in Zambia and how it compares to industry averages
• The company's portfolio of 60+ projects and their exploration approach combining AI with novel sensors
• The mining industry's historical under-investment in exploration technology that KoBold is addressing
• How critical metals like copper and lithium enable electrification and renewable energy

Likely interview topics

Based on the job description, expect questions about:

• How would you design a data system to handle real-time sensor data from field exploration alongside historical geological databases?
• Describe your experience managing teams that support both data scientists and domain experts - how do you prioritize competing needs?
• What strategies would you use to ensure data quality when collecting information from remote exploration sites?
• How have you scaled data infrastructure in previous roles to support growing AI/ML model development?
• What metrics would you track to measure the impact of your data systems on exploration outcomes?

Common mistakes to avoid

• Focusing only on generic data engineering experience without connecting it to scientific or resource discovery contexts
• Not demonstrating understanding of how data systems enable business outcomes in physical industries like mining
• Treating this as just another tech manager role without acknowledging the unique challenges of field data and geological expertise