Onsite Data Engineer, AI & Medical Imaging – CO

Medtronic plc; Lafayette (IN); On-site; USD 89,000 - 133,000; Full time

Benefits offered by this job

Health insurance

401(k) plan

Tuition assistance

Job summary

Medtronic CST in Lafayette, Colorado is seeking a Data Engineer to build and govern the data foundations that support AI and ML across surgical planning, navigation, and robotics programs. This onsite role partners with AI, product development, clinical, regulatory, and data science teams to ensure scalable data assets.

You will lead data acquisition, annotation management, data quality, and compliance activities, delivering well-organized datasets for algorithm development and validation.

Qualifications

• Bachelor’s degree with 2 years of relevant experience or Master’s degree.
• Experience with large-scale datasets in academic or industry settings.
• Proficiency in Python or SQL for data-focused programming.
• Ability to organize and prepare data from multiple sources.
• Experience using AI tools in professional, academic, or research contexts.

Responsibilities

• Lead data acquisition, curation, and preparation for programs.
• Design data collection strategies for clinical, lab, and external sources.
• Manage annotation programs and partners for labeled datasets at scale.
• Ensure data quality standards, validation, and governance.
• Collaborate with researchers and engineers to define data requirements for AI/ML initiatives.
• Support database administration and accessibility of datasets across projects.

Skills

Python

SQL

Data wrangling

AI tools

Data governance

Education

Bachelor’s degree or higher

Tools

Snowflake

AWS

Job description

Medtronic CST in Lafayette, Colorado is seeking a Data Engineer to build and govern the data foundations that support AI and ML across surgical planning, navigation, and robotics programs. This onsite role partners with AI, product development, clinical, regulatory, and data science teams to ensure scalable data assets.

You will lead data acquisition, annotation management, data quality, and compliance activities, delivering well-organized datasets for algorithm development and validation.