Description

Job Description

Required Qualifications

• 10+ years in data architecture, data engineering, or enterprise data platform roles, with 3+ years hands-on Databricks at enterprise scale.
• Deep, demonstrable expertise across the Databricks platform: Unity Catalog, Delta Lake, Spark, medallion architecture, and pipeline orchestration.
• Direct experience enabling Databricks Genie (AI/BI) or comparable self-service and conversational analytics capabilities, including the semantic and data modeling work that supports it.
• Proven track record setting enterprise-level data strategy and target-state architecture, not only implementing to someone else’s design.
• Strong command of at least one major cloud (AWS or Azure) and its integration with Databricks. Experience designing data governance frameworks: access control, lineage, cataloging, and data quality at scale.
• Ability to communicate architecture and strategy clearly to both executive and technical audiences.

Preferred Qualifications

• Life sciences or other regulated-industry experience (pharma, biotech, medical device, healthcare). Working knowledge of GxP-validated environments, 21 CFR Part 11, HIPAA, and data integrity (ALCOA+) requirements.
• Familiarity with life sciences data domains such as clinical (EDC, CTMS), laboratory (LIMS), manufacturing, real-world data, or genomics.
• Databricks certifications (for example, Databricks Certified Data Engineer Professional or Databricks Platform Architect).
• Experience with complementary tooling in the modern data stack (dbt, Fivetran, or similar) and with MLOps using MLflow

Key Skills

Education

Any Graduate