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Strong employer brand, metro location, and mid-level experience increase applicant competition.
Databricks and MLOps skills transfer across industries, but pharma regulatory experience raises specificity.
Numerous mandatory Databricks, MLOps, cloud and regulatory requirements create strict technical screening.
Design, build, and operate enterprise-scale data engineering and ML engineering systems on Databricks Lakehouse for analytics, data science, and AI/ML.
Develop and maintain production-grade data and ML pipelines with CI/CD, observability, and scalability across batch and real-time workloads.
Lead cloud migration and modernization initiatives involving AWS, Databricks, and Kubernetes; contribute to team standards and mentor junior engineers.
5+ years hands-on experience in data engineering and/or MLOps, preferably in biopharma or life sciences.
Deep hands-on experience with Databricks Lakehouse components (Delta Lake, Unity Catalog, Workflows, DLT, DABs) and cloud platforms (AWS or Azure).
Bachelor's, Master's, or Ph.D. in Computer Science, Data Engineering, Data Science, or related field.
Familiarity with healthcare/clinical data compliance (GxP, HIPAA) and pharmaceutical data governance; experience leading cloud migration or modernization at scale.
Experienced senior individual contributor with strong execution focus on end-to-end production data and ML pipelines in regulated domains.
Proficient in cross-geo collaboration with US teams and capable of defining and enforcing engineering standards in complex enterprise environments.
Skilled in leveraging AI-augmented tools (Claude Code, Copilot) and driving platform improvements to enhance engineering efficiency and reliability.