About the role
Company Description Guardant Health is a leading precision oncology company focused on guarding wellness and giving every person more time free from cancer. Founded in 2012, Guardant® is transforming patient care and accelerating new cancer therapies by providing critical insights into what drives disease through its advanced blood and tissue tests, real-world data and AI analytics. Guardant tests help improve outcomes across all stages of care, including screening to find cancer early, monitoring for recurrence in early-stage cancer, and treatment selection for patients with advanced cancer. For more information, visit guardanthealth.com and follow the company on LinkedIn, X (Twitter) and Facebook.
About
the Role Are you passionate about transforming healthcare through data-driven innovation? The Enterprise Data Platform team is seeking a hands-on Data Engineering Manager to lead the design, evolution, and scaling of our AWS-based enterprise data platform that powers analytics, operational intelligence, AI-enabled solutions, and business-critical decision-making. This role is ideal for a technical leader who combines strong data engineering expertise with architectural vision, product thinking, and a passion for solving business problems through data. As a player-coach, you will contribute to architecture, technical design, and key implementations while mentoring engineers and shaping the future of our data platform. You will partner closely with Engineering, Product, Analytics, Operations, and business stakeholders to build scalable data products and platform capabilities that transform data into actionable insights, intelligent automation, and measurable business outcomes.
Responsibilities Platform Architecture & Strategy Architect and evolve scalable, secure, and AI-ready data platforms across ingestion, storage, transformation, analytics, and data product layers. Define technical vision, architecture standards, and engineering best practices that balance business needs, scalability, governance, and innovation. Collaborate across Engineering, Product, Analytics, Operations, and business teams to define and execute the architectural vision for a modern data platform, influencing long-term technical direction while balancing current priorities with future growth. Lead the evolution of our AWS-centric data platform by continuously evaluating modern data architecture patterns, cloud-native services, and emerging AI technologies to ensure the platform scales efficiently with growing business needs and data volumes. Assess build-versus-buy opportunities and make pragmatic technology decisions that balance business value, scalability, maintainability, operational complexity, and cost. Champion modern data architecture patterns including data products, data contracts, observability, self-service capabilities, metadata-driven architectures, and AI-assisted engineering practices. Data Products & Business Impact • Lead the design and delivery of data products that generate measurable business value through analytics, automation, AI/ML, and operational insights. • Partner with stakeholders to identify high-impact opportunities and translate business challenges into scalable technical solutions. Apply a product mindset to data solutions, ensuring investments align with business priorities and user needs. Define success metrics and drive adoption, reliability, and business outcomes for data products. Engineering Leadership Lead the design and evolution of scalable data platforms and engineering practices that enable reliable delivery of data products across analytics, AI/ML, and operational use cases. Guide technology, architecture, and engineering decisions across the data ecosystem to ensure scalability, maintainability, operational excellence, and long-term platform evolution. Establish engineering standards for building reliable, scalable, and maintainable data platforms across batch, streaming, analytics, and AI-enabled workloads. Contribute to architecture reviews, design discussions, code reviews, and critical implementations. Promote engineering excellence through automation, testing, observability, CI/CD, Infrastructure-as Code, and operational best practices. Team & Culture Hire, mentor, and develop a high-performing team of data engineers. Coach engineers in system design, architectural thinking, engineering craftsmanship, and operational excellence. Foster a culture of ownership, innovation, continuous improvement, and technical curiosity. Governance & Reliability Ensure strong foundations in data quality, lineage, governance, privacy, security, and compliance. Drive platform reliability, monitoring, observability, and operational excellence. Partner with security and governance teams to implement best practices for handling sensitive and regulated data. Minimum