AWS Data Engineer - Databricks
Hybrid – Indianapolis, IN
About the Role
We are seeking a Data Engineer with 3–5 years of experience working specifically within the pharma industry to join a pharma-focused data team. This is a senior-flavored engineering role that combines hands-on pipeline and platform work with significant business-facing responsibility — including translating business needs into technical specs, presenting to executive-level stakeholders, and helping stand up new data domains from the ground up. You will design, build, and govern the data infrastructure that powers analytics and reporting across the business, while also acting as a trusted technical partner to non-technical stakeholders.
Key Responsibilities
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
Required Qualifications
Data Engineering & Pipelines
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
Platforms & Tools
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 Quality & Governance
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)
Pharma / Life Sciences Domain Knowledge
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)
Stakeholder Management
Requirements gathering and translation (business need â technical spec)
Cross-functional communication (Business â IT)
Executive-level presentation skills (given EC visibility)
Change management / adoption support
Analytical & Strategic Thinking
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
Project & Program Skills
Agile/Scrum familiarity
Documentation discipline (data dictionaries, source-to-target mappings)
Vendor/partner coordination (if external data sources are involved)
Nice-to-Have Differentiators
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