Data Engineer (Databricks/AWS)

• Design, build, and maintain scalable ETL/ELT pipelines (batch and streaming) using Databricks, AWS, and related orchestration tools
  
• Write and optimize advanced SQL, and build data transformations in Python or Scala
  
• Integrate external data sources via APIs and manage pipeline orchestration (Airflow, Databricks Workflows, AWS Glue)
  
• Apply data quality, governance, cataloging, and lineage practices aligned with regulated-industry standards
  
• Work within GxP-regulated data environments and apply awareness of data privacy/compliance considerations (e.g., 21 CFR Part 11, GDPR where applicable)
  
• Partner with business stakeholders across the pharma value chain (R&D, Manufacturing & Quality, Commercial, Drug Development) to gather and translate requirements into technical specifications
  
• Present technical work and data strategy to executive-level audiences
  
• Prioritize high-impact data initiatives and proactively identify and avoid duplicated data efforts
  
• Support change management and adoption of new data solutions across business teams
  
• Help stand up new data domains from scratch (green-field build), not just maintain existing ones
  

Requirements

• ETL/ELT development (batch and streaming)
  
• Advanced SQL (joins, window functions, query optimization)
  
• Python or Scala for data transformation
  
• Data pipeline orchestration (Airflow, Databricks Workflows, AWS Glue)
  
• API integration for external data source ingestion
  

• Databricks (Delta Lake, Unity Catalog, Genie)
  
• Cloud platforms — AWS (S3, Glue, Athena) and/or Azure/GCP equivalents
  
• Data warehousing concepts (dimensional modeling, star schema)
  
• BI/visualization tools (Tableau, Power BI, or similar) to understand downstream consumption
  

• Data profiling and cleansing techniques
  
• Metadata management and data cataloging
  
• Master data management (MDM) principles
  
• Data lineage tracking
  
• Data governance frameworks (especially regulated-industry standards)
  

• Familiarity with GxP-regulated data environments
  
• Understanding of the pharma value chain (R&D, Manufacturing & Quality, Commercial, Drug Development)
  
• Awareness of data privacy/compliance considerations (21 CFR Part 11, GDPR where applicable)
  
• Knowledge of common pharma data domains (clinical, manufacturing, quality, commercial)
  

• Requirements gathering and translation (business need → technical spec)
  
• Cross-functional communication (Business ↔ IT)
  
• Executive-level presentation skills (given EC visibility)
  
• Change management / adoption support
  

• Prioritization frameworks (identifying high-impact vs. low-value data asks)
  
• Cost-avoidance mindset (spotting duplication before it happens)
  
• Ability to work with ambiguity and evolving priorities
  

• Agile/Scrum familiarity
  
• Documentation discipline (data dictionaries, source-to-target mappings)
  
• Vendor/partner coordination (if external data sources are involved)
  

• Prior consulting or client-facing delivery experience
  
• Experience standing up new data domains from scratch (green-field vs. maintenance)
  
• Familiarity with AI/GenAI-enabled analytics tools