Position Summary At BMS, digital innovation and Information Technology are central to our vision of transforming patients lives through science.

To accelerate our ability to innovate and guarantee supply to our patients around the world, we must unleash the power of technology.

We are committed to being at the forefront of transforming the way medicine is made by harnessing the power of computer and data science, artificial intelligence, and other technologies to promote robust products and processes, faster decision making, and more efficient manufacturing and supply.

We are seeking an experienced and highly motivated data engineer to join the Digital Strategy and Process Optimization team within the Manufacturing Sciences & Technology (MS&T) organization.

In this role, the Process Data Engineering Senior Manager will be responsible for designing, building, and maintaining manufacturing data assets and data products to enable rapid investigation resolution and advanced multivariate model development for real-time process monitoring and control.

The ideal candidate will have exceptional background in data engineering, data systems, and data governance and will be comfortable working with both structured and unstructured data.

Experience in Biopharma manufacturing processes and data types is a plus, but not required.

If you want an exciting and rewarding career that is meaningful and directly helps deliver lifesaving medicines to patients, consider joining our diverse team Key Responsibilities Work as a member of the MS&T Data Engineering & Analytics team to develop and implement data engineering solutions that deliver high-quality, contextualized datasets as an enabler of advanced process modelling and other analytics Lead, mentor, and inspire a high-performing team of technical SMEs, fostering a culture of innovation, collaboration, and excellence Design and establish a scalable framework for engineering new features and processing modular datasets across different subject areas into modelling-ready data Oversee design, development, and maintenance of CI/CD pipelines on AWS to automate deployment, unit/integration testing, and scaling of data engineering pipelines Drive Infrastructure as Code best practices for cloud resource provisioning Collaborate with Data Enablement team within GPS Business Insights & Technology (BIT) to shape data and technology strategy and drive towards synergistic outcomes Optimize or redesign existing data engineering solutions to improve efficiency or scalability Devise and implement data engineering best practices across the team with a focus on short-term deliverables and strategic capabilities Perform code reviews to ensure code quality and adherence to best practices Leverage the latest advances in data engineering and analytics to design innovative solutions Learn new technologies and lead proof-of-concepts to further innovate and optimize data engineering approaches Acquire and maintain thorough understanding of internal and external manufacturing data landscape, including enterprise and site systems, data warehouses, and data lakes Qualifications & Experience Expected 9 years, 4 years with Ph.

D., of experience in data engineering or Dev Ops environment Minimum Bachelors degree in computer science, information systems, computer engineering, or equivalent experience Demonstrated success in the leadership and development of experienced and specialized technical SMEs Advanced knowledge of Python or similar data engineering focused programming language Hands-on experience implementing and operating cloud-based data ingestion, integration, transformation, storage, and virtualization solutions using company approved technologies such as AWS (Amazon Web Services) native services (S3, Glue, Athena, Redshift, RDS, Aurora, Lambda, etc.), Cloudera Data Platform (CDP), and Domino In-depth experience with distributed processing systems like Apache Spark Experience in Data Ops workflow orchestration tools such as Apache Airflow, dbt, etc.

Deep experience and knowledge of: Software engineering principles: testing (definition of unit tests, integration tests), setting up CI/CD pipelines using cloud-based tools like AWS Code Pipeline, managing and orchestrating containers with Docker and Kubernetes, experience developing or interacting with APIs Data quality and validation principles: experience with libraries like great-expectations, pandera, pydantic, pandas profiler Git-based code repository and version control principles with a strong understanding proper branching strategies to ensure seamless code collaboration and integration Infrastructure as Code principles, leveraging tools like AWS Cloud Formation and Terraform Data architecture principles: data modeling, SQL query optimization, data warehouse design patterns Security principles: data encryption, access control, authentication and authorization Team management skills: strong track record of leading teams in the technical development of analytical solutions Experience integrating with Spotfire or other visual analytics platforms like Tableau Deep experience in definition and implementation of feature engineering Good experience with agile/scrum development processes and concepts and with leveraging project management tools like Jira and Confluence Experience managing multiple priorities and working in fast-paced, constantly evolving environment with a variety of cross-functional teams Evaluates complex issues through analytical thinking and previous experience to consider short- and long-term implications and interdependencies Excellent interpersonal, collaborative, team building, and communication skills to ensure effective collaborations within matrix teams.

Demonstrated performance against cooperation principles and enterprise mindset.

Experience working in life sciences/biopharmaceutical industry is a plus.

GPSHYD LI-Hybrid