Principal Data Engineer, MS&T Digital Strategy and Process Optimization at Bristol Myers Squibb
Ireland, , Ireland -
Full Time


Start Date

Immediate

Expiry Date

05 Jun, 25

Salary

0.0

Posted On

06 Mar, 25

Experience

4 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Collaboration, Amazon Web Services, Data Modeling, Design Patterns, Glue, Dbt, Aws, Confluence, Access Control, Cdp, Analytical Solutions, Kubernetes, Query Optimization, Athena, Authorization, Spotfire, Jira, Data Engineering, Management Skills, Team Building

Industry

Information Technology/IT

Description

WORKING WITH US

Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams rich in diversity. Take your career farther than you thought possible.
Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us .

POSITION SUMMARY

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 Principal Data Engineer 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 (including software engineering principles), 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 Digital Strategy and Process Optimization team to develop and implement data engineering solutions that deliver high-quality, contextualized datasets as an enabler of advanced process modelling and other analytics
  • Design and establish a scalable framework for engineering new features and processing modular datasets across different subject areas into modelling-ready data
  • Optimize or redesign existing data engineering solutions to improve efficiency, velocity, and/or scalability, specifically by incorporating software engineering principles
  • Collaborate with Data & Supply Technology Excellence (DSTE) team within GPS IT to shape data and technology strategy and drive towards synergistic outcomes
  • Devise and implement data engineering best practices across the team with a focus on short-term deliverables and strategic capabilities
  • Partner with and guide offshore data partner team who provides support in implementing, maintaining, and supporting data engineering pipeline
  • Mentor fellow Data Engineers where required
  • 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 DevOps environment
  • Minimum Bachelor’s degree in computer science, information systems, computer engineering, or equivalent experience
  • 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, SageMaker, EMR, CodeBuild, etc.), Cloudera Data Platform (CDP), and Domino
  • In-depth experience with distributed processing systems like Apache Spark
  • Experience in DataOps workflow orchestration tools such as Apache Airflow, dbt, Dagster, etc.
  • Deep experience and knowledge of:
  • Software engineering principles: code versioning, testing (definition of unit tests, integration tests), setting up CI/CD pipelines in collaboration with DevOps teams, experience with Docker containers and kubernetes, experience developing or interacting with APIs
  • Data quality and validation principles: experience with libraries like great-expectations, pandera, pydantic, pandas profiler
  • 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

Why you should apply

  • You will help patients in their fight against serious diseases
  • You will be part of a company that encourages excellence and innovation, respects diversity, develops leaders and values its employees
  • You’ll get a competitive salary and a great benefits package including an annual bonus, pension contribution, family medical assurance, 27 day annual leave, life assurance and on-site gym
Responsibilities
  • Work as a member of the MS&T Digital Strategy and Process Optimization team to develop and implement data engineering solutions that deliver high-quality, contextualized datasets as an enabler of advanced process modelling and other analytics
  • Design and establish a scalable framework for engineering new features and processing modular datasets across different subject areas into modelling-ready data
  • Optimize or redesign existing data engineering solutions to improve efficiency, velocity, and/or scalability, specifically by incorporating software engineering principles
  • Collaborate with Data & Supply Technology Excellence (DSTE) team within GPS IT to shape data and technology strategy and drive towards synergistic outcomes
  • Devise and implement data engineering best practices across the team with a focus on short-term deliverables and strategic capabilities
  • Partner with and guide offshore data partner team who provides support in implementing, maintaining, and supporting data engineering pipeline
  • Mentor fellow Data Engineers where required
  • 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 lake
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