Staff Data Engineer

The Company Dexcom Corporation (NASDAQ DXCM) is a pioneer and global leader in continuous glucose monitoring (CGM). Dexcom began as a small company with a big dream: To forever change how diabetes is managed. To unlock information and insights that drive better health outcomes. Here we are 25 years later, having pioneered an industry. And we're just getting started. We are broadening our vision beyond diabetes to empower people to take control of health. That means personalized, actionable insights aimed at solving important health challenges. To continue what we've started: Improving human health.   We are driven by thousands of ambitious, passionate people worldwide who are willing to fight like warriors to earn the trust of our customers by listening, serving with integrity, thinking big, and being dependable. We've already changed millions of lives and we're ready to change millions more. Our future ambition is to become a leading consumer health technology company while continuing to develop solutions for serious health conditions. We'll get there by constantly reinventing unique biosensing-technology experiences. Though we've come a long way from our small company days, our dreams are bigger than ever. The opportunity to improve health on a global scale stands before us. Meet the team: As part of the Commercial Data Science & Revenue Operations team, you will join a data engineering group responsible for building and operating Dexcom’s commercial data platform on AWS. As a Staff Data Engineer, you will design and build data services that ingest from 12+ vendor sources, orchestrate transformation pipelines, and deliver analytics-ready datasets to data scientists, analysts, and 300+ field representatives. You will own AWS-native development workflows using Lambda, Glue, CDK, Step Functions, and Redshift, with a focus on reliability, automation, and scalable infrastructure. The ideal candidate will partner with data scientists and business stakeholders to build the platform that powers commercial analytics and AI/ML initiatives. Where you come in: Design and build data pipelines that ingest, transform, and deliver vendor data (SFTP, API, file-based sources) into S3 and Redshift, with validation checks and automated error handling at each stage. Own AWS-native development workflows leveraging Lambda, Glue, CDK, and Step Functions to create scalable, cost-aware data services that integrate cleanly with enterprise platforms and reduce manual operational overhead. Implement data quality assertion frameworks that validate completeness, freshness, and accuracy at pipeline boundaries before data reaches downstream consumers — data scientists, analysts, and commercial teams. Build alerting systems and reliability playbooks that enable the team to detect and respond to data platform issues before they impact downstream consumers, shortening mean time to detect/resolve. Model and evolve the data warehouse schema , designing dimensional models and orchestrating multi-source feeds into a unified analytical layer with documented lineage and managed schema evolution. Partner with Privacy, Security, and IT on data governance requirements, translating policy requirements into technical implementations that keep the platform compliant without slowing engineering velocity. Set technical direction for your area and mentor team members through code reviews, pairing sessions, and documentation of reusable patterns and architectural decisions. What makes you successful: Cloud: Hands-on proficiency with AWS-native architectures (Lambda, Glue, S3, Redshift, Step Functions, CDK) with cost optimization awareness and ability to make build-vs-buy trade-off recommendations. Active AWS certification (Solutions Architect - Associate, Data Engineer - Associate, or equivalent) preferred. Data Platform: Experience designing dimensional models and managing schema evolution in a growing data platform serving multiple stakeholder groups, with