Senior Manager, Machine Learning Platform Engineer

At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference. Every member of Gilead’s team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we’re looking for the next wave of passionate and ambitious people ready to make a direct impact. We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together. Job Description This Data Platform Engineer will have the unique opportunity to apply cutting-edge data and analytics technologies to one of the most meaningful challenges in healthcare: ensuring the quality of medicines that improve and save lives. As a pivotal member of R&D Quality, this role will help transform how quality insights are generated, scaled, and acted upon across Gilead's drug development and clinical research programs. Through the development of robust data pipelines and advanced analytics platforms, the successful candidate will enable more proactive quality oversight, smarter decision-making, and continuous improvement, ultimately supporting Gilead's mission to deliver life-changing therapies to patients worldwide. The Data Platform Engineer will partner with the Quality Analytics & Insights team, a small, high-impact group responsible for advancing data and analytics capabilities across R&D Quality. This role will build and maintain the data infrastructure that supports Quality Performance and Quality Health analytics focused on signal detection, risk analytics, early identification of emerging issues, mitigation strategies, and continuous improvement. Working closely with analytics professionals, the engineer will transform analytical prototypes into scalable, production-ready solutions through robust data pipelines, cloud infrastructure, monitoring, and automation. The role will collaborate directly with Quality teams, IT, and global delivery teams to support key Quality System elements and programs, including Audit, Deviation, CAPA, Risk Management, Escalation/Serious Breach, and Quality Analytics, while helping define the technology roadmap for next-generation analytics and automation across the organization. Primary Responsibilities Data Engineering Technical Ownership: Operate as a self-directed contributor who scopes, plans, and drives initiatives end-to-end — translating ambiguous Quality problems into technical solutions, making sound architectural trade-offs, and delivering production outcomes with minimal oversight. Infrastructure & Environment Automation: Independently provision and manage cloud infrastructure using infrastructure-as-code and containerization, standing up reproducible, scalable environments for data processing, application deployment, and analytics with minimal reliance on external teams. Application Lifecycle Management: Develop and maintain pipelines to transition analytics applications and data products from development to production, including packaging, CI/CD, automated testing, and deployment. Support reliable delivery of Quality use cases such as signal detection, risk analytics, and Quality Performance/Quality Health analytics. Data Pipeline Development: Design robust batch and streaming data workflows; integrate, define, and manage data sets, lineage, and reuse across QMS data sources (e.g., Audit, Deviation, CAPA, Risk Management). Data Orchestration: Author and schedule reliable, observable workflows using orchestration tools and distributed processing, ensuring dependencies, retries, and SLAs are handled without manual intervention. Production Operations & Monitoring: Ensure the reliability and scalability of data pipelines and analytics applications; implement effective logging, tracing, and alerting. Establish monitoring for data quality, pipeline performance, and service health, paying particular attention to QMS data feeds, where low-frequency quality signals amplify the impact of anomalies. Collaboration & Enablement Cross-functional Partnership: Work closely with analytics professionals, Quality analysts, and stakeholders across R&D Quality programs (e.g., Audit, Deviation, CAPA, Risk Management, Escalation/Serious Breach). Provide frameworks, templates, and guardrails that accelerate analytics delivery. Testing & Validation: Demonstrate a strong focus on testing by setting up frameworks for data pipelines, analytics applications, and production code. Design validation pipelines with automated quality gates, including type checking, linting, integration tests, and contract tests. Documentation & Release Management: Develop clear, detailed guides, operational playbooks, and user instructions. Coordinate releases with IT and the global team; maintain runbooks, rollback strategies, and change tickets. Security & Compliance: Apply security, access-control, and data-governance best practices across pipelines and infrastructure, ensuring solutions meet the expectations of a validated, GxP-regulated environment. Innovation & Technical Strategy Technology Evaluation & Roadmap Input: Evaluate emerging data, cloud, and analytics tooling; prototype promising approaches and recommend adoption, contributing to the technical roadmap for next-generation Quality analytics and automation. Tech Stack Basic Programming & scripting: Python and SQL; scripting with Python, Bash, or PowerShell. Source control: Git and source control management. CI/CD & release management: Working knowledge of CI/CD tools and release management (e.g., GitHub Actions). Cloud platforms: Hands-on experience with a major cloud provider (AWS or Azure). Containers: Container technologies (Docker; Kubernetes). Data platform: Databricks. Preferred Cloud infrastructure / infrastructure-as-code: Terraform; broader cloud engineering experience (AWS preferred). Monitoring & logging: Datadog, Splunk, CloudWatch, or Prometheus. Infrastructure concepts: Understanding of networking, security, and infrastructure fundamentals. Basic Qualifications: Bachelor's Degree and Eight Years' Experience OR Masters' Degree and Six Years' Experience OR PhD / PharmD Preferred Qualifications: Degree in computer science, computer engineering, information systems, or a related discipline with relevant experience in data engineering, platform engineering, or data operations Significant hands-on experience operationalizing data and analytics solutions end-to-end, including data engineering, pipeline development, deployment, and production monitoring. Strong programming skills in key languages such as Python, SQL, Go, and TypeScript, with proven ability to manipulate large and complex datasets using distributed computing technologies. Familiarity with AWS cloud services. Strong troubleshooting and problem-solving skills. Excellent verbal and written communication skills, with the ability to present complex findings to both technical and non-technical audiences and a strong orientation toward teamwork in a fast-paced, regulated environment. Experience building, packaging, and maintaining data processing libraries and analytics applications in production. Experience with CI/CD, infrastructure-as-code, and cloud-based data platforms. Proficiency with Databricks distributed processing (Spark), data orchestration, and similar data and BI tech