Senior Data Engineer, Research IT — Remote-friendly Pipelines

Bristol Myers Squibb

Princeton (NJ)

On-site

USD 138,000 - 167,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

Health Coverage

401(k) Plan

Paid Time Off

Wellbeing Programs

Job summary

Bristol Myers Squibb in Princeton, NJ is seeking a Senior Data Engineer to support Foundational Data Products including CoreReg, Substance Mart, DARE, and Asset Mart. You will design, develop, and maintain scalable data pipelines, ensuring data quality and enabling data-driven decisions across research IT.

Collaborate with data scientists, researchers, and IT professionals to deliver impactful solutions, optimize performance, and document data models.

Qualifications

Bachelor's or master's degree in Computer Science, Information Technology, or a related field.
Minimum of 5–7 years of experience in data engineering or a related role.
Proficiency in Databricks with a strong focus on data engineering.
Strong SQL skills and experience with relational databases.
Programming languages such as Python, Java, or Scala.
Familiarity with cloud platforms (e.g., AWS, Azure, GCP).
Experience with data warehousing and data lakehouse solutions (e.g., Redshift, BigQuery, Snowflake).
Experience with ETL tools (e.g., Glue Studio, Informatica PowerCenter).
Excellent problem-solving and communication skills; ability to work effectively in a collaborative team environment.

Responsibilities

Data Pipeline Development: Design, develop, and maintain robust and scalable data pipelines supporting Foundational Data Products (CoreReg, Substance Mart, DARE, and Asset Mart).
Data Integration: Integrate data from diverse sources, ensuring consistency, quality, and accessibility across platforms.
Data Management: Implement best practices in data governance, data quality, and data security.
Collaboration: Partner closely with data scientists, researchers, and cross-functional stakeholders to understand data requirements and deliver impactful solutions.
Performance Optimization: Optimize data processing workflows for performance and scalability.
Documentation: Create and maintain comprehensive documentation for data pipelines, data models, and integration processes.
Troubleshooting: Proactively identify and resolve data-related issues, ensuring reliability and accuracy.
Innovation: Stay current with the latest technologies and best practices in data engineering and apply them to continuously improve processes.