Analytics Engineer III

Working with UsChallenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible.Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us.SummaryThe Analytics Engineer III is a senior individual contributor role within BIT Hyderabad, owning the design, build, and operation of both data engineering and ML engineering systems that power analytics, data science, and AI/ML at scale across BMS. The AE-3 is hands-on and execution-focused — delivering enterprise-grade data products, ML pipelines, and platform infrastructure while collaborating closely with US counterparts, data scientists, and platform teams. They also contribute to team standards, code reviews, and junior engineer growth as a natural part of the role.Roles & ResponsibilitiesData & Lakehouse EngineeringDesign and operate end-to-end data products — ingestion → medallion architecture → transformation → serving → CI/CD → observability — on Databricks at enterprise scaleBuild production-grade Lakehouse pipelines using Delta Lake (OPTIMIZE, ZORDER, liquid clustering, CDF), Unity Catalog, Workflows, Delta Live Tables, and Structured StreamingDeploy using Databricks Asset Bundles (DABs); write modular Python code with secure credential management via service principals, secret scopes, and IAMDefine and enforce data engineering standards — versioned pipelines, data contracts, automated quality gates, lineage, and standardized project templatesDrive Databricks optimization — cluster sizing, Photon, autoscaling, and SQL warehouse tuningML Engineering & MLOpsDesign and operate end-to-end ML pipelines — feature engineering, model training, evaluation, deployment, serving, and monitoring — with emphasis on scalability and reliabilityBuild and maintain MLOps platform components — experiment tracking, model registries, CI/CD for ML, feature stores, and containerized environmentsDefine CI/CD strategy for ML — model validation gates, canary/shadow deployments, automated rollback, and high-availability inference patternsManage production ML pipeline schedules across batch and real-time inference — SLA adherence, issue triage, and incident resolutionObservability & PlatformArchitect observability across data and ML systems — Great Expectations, Pandera, Evidently, Databricks Lakehouse Monitoring, Prometheus, Grafana — covering data quality, model drift, and SLAs/SLOsLead cloud migration and modernization to AWS, Databricks, and Kubernetes-based architecturesDevelop reusable libraries, templates, and frameworks to reduce engineering toil for data scientists and peersLeverage AI tools (Claude Code, Copilot) to accelerate delivery and build reusable skills/agentsCollaboration & StandardsPartner with Data Science, MLOps, IT, and US counterparts on execution, planning, and deliveryConduct code and design reviews; contribute to team standards and help unblock peersEnsure compliance with GxP, HIPAA, and pharmaceutical data governance standards with Unity Catalog as the governance backboneCommunicate technical decisions clearly through documentation, runbooks, and RFCsSkills & CompetenciesDomainKey SkillsDatabricksDelta Lake, Unity Catalog, Workflows, DLT, Databricks SQL, Structured Streaming, DABs, Lakehouse MonitoringMLOps ToolingMLflow, Dagster/Airflow/Kedro, DVC, Feast, Hydra/OmegaConfCloud & InfraAWS (SageMaker, EKS, S3, IAM) and/or Azure (AzureML, AKS, ADLS); Kubernetes; DockerCI/CD & HygieneGitHub Actions, pre-commit, Ruff, nox, uv/Poetry, PytestProgrammingExpert Python & SQL; PySpark; data modeling (dimensional, Data Vault, medallion)Data Toolingdbt, Polars, Pandas, DuckDBML ServingFastAPI, BentoML, Triton, KServeMonitoringEvidently, Great Expectations, Pandera, Prometheus, GrafanaAI-AugmentedClaude Code, Copilot; LLMOps, RAG, vector databasesGovernanceUnity Catalog, IAM, secrets management, GxP/HIPAAExperienceBachelor's, Master's, or Ph.D. in Computer Science, Data Engineering, Data Science, or related field5+ years hands-on data engineering and/or MLOps experience, preferably in biopharma or life sciencesProven track record building and owning enterprise-scale data products and ML pipelines end-to-end in productionDeep Databricks hands-on — Lakehouse, Delta Lake optimization, Unity Catalog, Workflows, DABsExperience leading cloud migration or modernization to Databricks, AWS, or Kubernetes at scaleFamiliarity with healthcare/clinical data, regulatory considerations (GxP, HIPAA), and pharmaceutical data governanceExperience with AI-augmented engineering tools (Claude Code, Copilot) to streamline workflowsCross-geo collaboration with US-based teams strongly preferredDatabricks Certification (Data Engineer Associate/Professional) or cloud certification (AWS / Azure / GCP)If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.Uniquely Interesting Work, Life-changing CareersWith a single vision as inspiring as “Transforming patients’ lives through science™ ”, every BMS employee plays an integral role in work that goes far beyond ordinary. Each of us is empowered to apply our individual talents and unique perspectives in a supportive culture, pr