Data Engineer - Life Sciences Sector - Manager - Consulting - Location OPEN

Ernst & Young - Trenton, NJ

Full-time - Manager

Trenton, NJ

Professional, Scientific, and Technical Services

About the position

The Data Engineer - Life Sciences Sector - Manager position at EY involves leading a team to implement innovative data engineering solutions for clients in the Life Sciences sector. This role emphasizes collaboration with clients and interdisciplinary teams to address complex challenges using cutting-edge technology. The position requires a strong focus on continuous learning and adapting to industry trends, with significant client interaction and the opportunity to shape one's career path based on individual skills and ambitions.

Responsibilities

• Collaborate with clients to deploy the latest data engineering technologies and methodologies.
• Design and build robust, scalable data solutions for Life Science clients.
• Integrate and enrich data from various sources across a broad technology landscape.
• Provide guidance and perform technical development tasks to ensure data engineering solutions are properly engineered and maintained.
• Engage with scientists and engineers to solve computational problems for clients.
• Deliver quality client services focusing on complex and specialized issues surrounding emerging technology.

Requirements

• Bachelor's degree and 6-10 years of full-time working experience in Data Engineering, Big Data, and related fields.
• 2-4 years of experience directly managing technical teams.
• At least 2 years working in the Life Science domain, developing data solutions for challenges in drug discovery, clinical trials, or patient data management.
• Strong skills in Python and other data manipulation languages.
• Experience with data engineering models and frameworks, including data ingestion, transformation, and insight generation.
• Extensive experience with DevOps tools like GIT, Azure DevOps, and Agile tools such as Jira.
• A solid understanding of data engineering workflows, including data collection, transformation, and analysis.
• Experience with data engineering methods and platforms such as Apache Airflow or similar.
• Experience with CI/CD and test-driven development.