Analytics Engineer III

• Design and operate end-to-end data products - ingestion → medallion architecture → transformation → serving → CI/CD → observability - on Databricks at enterprise scale
• Build production-grade Lakehouse pipelines using Delta Lake (OPTIMIZE, ZORDER, liquid clustering, CDF), Unity Catalog, Workflows, Delta Live Tables, and Structured Streaming
• Deploy using Databricks Asset Bundles (DABs); write modular Python code with secure credential management via service principals, secret scopes, and IAM
• Define and enforce data engineering standards - versioned pipelines, data contracts, automated quality gates, lineage, and standardized project templates
• Drive Databricks optimization - cluster sizing, Photon, autoscaling, and SQL warehouse tuning

• Design and operate end-to-end ML pipelines - feature engineering, model training, evaluation, deployment, serving, and monitoring - with emphasis on scalability and reliability
• Build and maintain MLOps platform components - experiment tracking, model registries, CI/CD for ML, feature stores, and containerized environments
• Define CI/CD strategy for ML - model validation gates, canary/shadow deployments, automated rollback, and high-availability inference patterns
• Manage production ML pipeline schedules across batch and real-time inference - SLA adherence, issue triage, and incident resolution

• Architect observability across data and ML systems - Great Expectations, Pandera, Evidently, Databricks Lakehouse Monitoring, Prometheus, Grafana - covering data quality, model drift, and SLAs/SLOs
• Lead cloud migration and modernization to AWS, Databricks, and Kubernetes-based architectures
• Develop reusable libraries, templates, and frameworks to reduce engineering toil for data scientists and peers
• Leverage AI tools (Claude Code, Copilot) to accelerate delivery and build reusable skills/agents

• Partner with Data Science, MLOps, IT, and US counterparts on execution, planning, and delivery
• Conduct code and design reviews; contribute to team standards and help unblock peers
• Ensure compliance with GxP, HIPAA, and pharmaceutical data governance standards with Unity Catalog as the governance backbone
• Communicate technical decisions clearly through documentation, runbooks, and RFCs

• Bachelor's, Master's, or Ph.D. in Computer Science, Data Engineering, Data Science, or related field

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hands-on data engineering and/or MLOps experience, preferably in biopharma or life sciences

• Proven track record building and owning enterprise-scale data products

ML pipelines end-to-end in production

• Deep Databricks hands-on - Lakehouse, Delta Lake optimization, Unity Catalog, Workflows, DABs
• Experience leading cloud migration or modernization to Databricks, AWS, or Kubernetes at scale
• Familiarity with healthcare/clinical data, regulatory considerations (GxP, HIPAA), and pharmaceutical data governance
• Experience with AI-augmented engineering tools (Claude Code, Copilot) to streamline workflows
• Cross-geo collaboration with US-based teams strongly preferred
• Databricks Certification (Data Engineer Associate/Professional) or cloud certification (AWS / Azure / GCP)

We hire for skills and capabilities, not just credentials - if this role excites you, but doesn't perfectly match your resume, we encourage you to apply anyway.

How We Work

Where you work matters - because collaboration, innovation and patient impact happen in many settings. Our roles are structured across four work models: site-essential, site-by-design, field-based and remote-by-design. The model assigned to this role is based on its core responsibilities. Learn more at https://careers.bms.com/ways-of-working.