Analytics Engineer III

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.

The 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 & Responsibilities Data & Lakehouse Engineering 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 ML Engineering & MLOps 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 Observability & Platform 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 Collaboration & Standards 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 Skills & Competencies DomainKey Skills Databricks Delta Lake, Unity Catalog, Workflows, DLT, Databricks SQL, Structured Streaming, DABs, Lakehouse Monitoring MLOps Tooling MLflow, Dagster/Airflow/Kedro, DVC, Feast, Hydra/OmegaConf Cloud & Infra AWS (SageMaker, EKS, S3, IAM) and/or Azure (AzureML, AKS, ADLS); Kubernetes; Docker CI/CD & Hygiene GitHub Actions, pre-commit, Ruff, nox, uv/Poetry, Pytest Programming Expert Python & SQL; PySpark; data modeling