Jobs at Dexcom

As the Analytics Engineer within the R&D Engineering organization, you will be responsible for standardizing, integrating, and automating engineering data and data management practices across mechanical, electrical, and firmware engineering teams. This role focuses on transforming complex, multi-disciplinary data into trusted, scalable analytics and business intelligence solutions. You will model and integrate diverse data types and sources, develop advanced dashboards, and enable data-driven decision-making across the product development lifecycle. In addition, you will play a key role in delivering AI solutions—both custom-built and third-party—by partnering closely with engineering, IT, and business stakeholders.

Your contributions will help improve data integrity, visibility, and efficiency, enabling R&D teams to operate at scale while supporting innovation in a regulated medical device environment.

• Lead efforts to standardize and automate engineering data pipelines across mechanical, electrical, hardware, and firmware domains.
• Design, model, and integrate structured and unstructured data from multiple engineering and enterprise systems.
• Build and maintain business intelligence dashboards and analytics (e.g., Power BI, Tableau) that translate complex technical data into clear, actionable insights for R&D and leadership through visualizations and executive-level reporting.
• Partner with engineering and IT stakeholders to define data governance, data quality standards, and scalable data management practices.
• Write and maintain queries, transformations, and automations using SQL and related programming languages.
• Support data modeling and storage solutions using platforms such as MongoDB and relational databases.
• Drive the evaluation, pilot, and implementation of AI solutions, including custom models and third-party platforms, to enhance engineering productivity and insight.
• Analyze and interpret engineering test and validation data using statistical methods to identify trends, variability, and performance drivers across products and systems.
• Develop statistical models and summaries to support engineering decision-making, trade studies, and design verification activities.
• Collaborate cross-functionally to identify opportunities where data analytics and AI can improve engineering processes, quality, and decision-making.

• Proven experience in a data analyst or data scientist role supporting engineering, R&D, or technical organizations.
• Proficient in data modeling, querying, and transformation using SQL and NoSQL technologies such as MongoDB (desired).
• Strong experience developing dashboards and analytics in Power BI and/or Tableau.
• Comfortable programming for data integration, automation, and analytics workflows.
• Understanding of engineering data types and workflows across mechanical, electrical, hardware, and firmware development.
• Experience deploying or supporting AI and machine learning solutions, including integration of third-party AI tools.
• Problem solver who thrives on standardizing processes, improving data integrity, and reducing manual effort.
• Communicates clearly and effectively, collaborating with engineers, data consumers, and leadership.
• Detail-oriented, organized, and capable of managing multiple data initiatives in a fast-paced, regulated environment, with an eye for process improvement and an interest in standardization, efficiency gains, and automation within engineering workflows.
• Experience with Onshape is a plus, especially in environments transitioning from traditional CAD tools, along with exposure to CAD data migration or platform adoption efforts.
• Typically requires a Bachelor's degree in a technical discipline and a minimum of 3-5 years related experience, or a Master's degree and 2 years equivalent industry experience, or a PhD and 0-2 years experience.