Data Engineer

Quick Facts

• Role: Data Engineer (AI & Data team)

• Focus: Building and maintaining data pipelines for drug development analytics and machine learning

Description

Design, develop, and maintain ETL/ELT pipelines that extract, transform, and load data into data lakes and warehouses. Implement transformation rules and data models to support analytics and ML workflows, ensuring data quality and maintainable data catalogs. Work with product analysts, data scientists, and ML engineers in a learning-driven AI/GenAI environment.

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines for diverse data sources

• Implement data transformation rules and develop data models for analytics/ML

• Collaborate with cross-functional data and product teams to make data accessible

• Ensure data quality; maintain data catalogs; use orchestration, logging, and monitoring

• Use test-driven development for reliable data solutions

• Contribute to information architecture and follow Git-based version control best practices

Requirements

• Strong expertise in Databricks and AWS services (S3, IAM, Redshift, Glue, Lambda, Step Functions, CloudWatch)

• Hands-on experience with ETL/ELT processes and orchestration tools

• Proficiency in Python and SQL

• Experience with GitHub and CI/CD pipelines using GitHub Actions

• Excellent communication skills and ability to document processes

Benefits

• Work on real-world data, AI, and GenAI applications with tangible impact on drug development and patient care

• Continuous learning culture with certifications, workshops, and hands-on projects

• Modern tech stack (Databricks, AWS, Spark, Python, CI/CD pipelines) and opportunities to experiment

• Training, knowledge sharing, and collaboration with AI and data experts