Director, Data Engineering Lead - Digital Insights

Job description

Job Description DSCS Digital Technologies: Digital Insights - Director, Data Engineering Lead We are a global biopharmaceutical leader with a portfolio of prescription medicines, oncology, vaccines and animal health products. We are driven by our purpose to develop and deliver innovative products that save and improve lives. With 69,000 employees operating in more than 140 countries, we offer state of the art laboratories, plants and offices that are designed to inspire our employees as we learn, develop and grow in our careers. We are proud of our over 125 years of service to humanity and continue to be one of the world’s biggest investors in Research & Development. We are seeking a Director, Data Engineering Lead to join our Digital Insights (DI) team within the Development Sciences and Clinical Supply (DSCS) Digital Technologies organization (DDT). Digital is the multiplier that will allow DSCS to deliver better experiments faster, efficient filing and launch, more robust supply chains and higher-confidence decisions across the portfolio. The DSCS Digital Technologies organization is responsible for the invention and application of new digital tools/workflows to support scientists across drug substance development, drug product development and analytical development. We aspire to embed digital technologies into the fabric of DSCS culture to drive transformational impact across the CMC space. In this role, the Director, Data Engineering Lead will lead a small team of data engineers responsible for transforming complex scientific and operational data into trusted, analysis-ready data products and digital workflows. This Director, Data Engineering Lead will sit on the Digital Insights leadership team to set data engineering strategy and project prioritization for Digital Insights. They will partner with DI and DDT leadership to ensure connectivity across matrixed data engineering efforts. This role serves as a critical bridge across scientific domains, digital platforms, and advanced analytics, ensuring that data is structured, contextualized, FAIR, and AI-ready. The successful candidate will build, develop, and lead a high-performing data engineering team. This team will create scalable data pipelines for laboratory data, harmonized data models, ontologies, and reusable digital assets. Responsibilities will include supporting biologics development, including analytical characterization, drug substance and drug product process development, laboratory operations, predictive sciences, and emerging AI applications, with subsequent expansion to other modalities. The role demands strong technical leadership, organizational influence, and the ability to partner closely with scientists, data scientists, software engineers, and business stakeholders across CMC and IT, thereby accelerating digital transformation. Primary Responsibilities Lead, coach, and develop a team of business side data engineers supporting prioritized programs. Establish data engineering standards, development practices, and code quality expectations. Foster a collaborative, innovative, and customer-focused team culture. Manage resource allocation, prioritization, and execution across concurrent initiatives. Partner with the Digital Insights leadership team to define and execute the Digital Insights data engineering strategy aligned with DDT objectives. Lead the design, development, and maintenance of scalable, reliable, and reusable data pipelines. Drive development of domain-specific and cross-domain data products supporting analytics, visualization, modeling, and AI use cases. Lead implementation of data quality monitoring, lineage, metadata management, and governance practices. Partner with laboratory scientists, process developers, pipeline leaders, modelers, and digital product teams to understand scientific workflows and translate requirements into data solutions. Accelerate availability of scientific data from laboratory instruments, ELNs, manufacturing systems, and enterprise platforms. Enable self-service access to governed scientific data and insights. Support use cases involving predictive analytics, machine learning, digital twins, and agentic AI solutions. Partner with IT to define target-state architectures for data ingestion, transformation, storage, and consumption. Collaborate with enterprise IT and data governance teams to ensure alignment with corporate standards. Communicate technical concepts effectively to both technical and non-technical audiences. Influence strategic decisions through data-driven recommendations and roadmap planning. Help coordinate broader DDT data engineering community of practice. Required Qualifications Education Ph.D. in Chemical Engineering, Biochemical Engineering, Biochemistry, Engineering, Chemistry, Biology, Pharmaceutical Sciences, or a closely-related field with at least 6 years of industrial/pharmaceutical or relevant experience. M.S. in Chemical Engineering, Biochemical Engineering, Biochemistry, Engineering, Chemistry, Biology, Pharmaceutical Sciences, or a closely-related field with at least 8 years of industrial/pharmaceutical or relevant experience. B.S. in Chemical Engineering, Biochemical Engineering, Biochemistry, Engineering, Chemistry, Biology, Pharmaceutical Sciences, or a closely-related field with at least 10 years of industrial/pharmaceutical or relevant experience. Experience Pharmaceutical, biotechnology, life sciences, or healthcare industry experience. Experience supporting laboratory, manufacturing, process development, or scientific research environments. Knowledge of scientific data platforms, laboratory informatics systems, and instrument-generated data. Experience in data engineering, software engineering, analytics engineering, or a related technical discipline. Demonstrated experience leading technical teams and managing scientific or enterprise data programs. Experience designing and deploying enterprise-scale data pipelines and data products. Proven success delivering complex cross-functional initiatives. Expertise in data engineering technologies such as Databricks, cloud data platforms, SQL, Python, and modern ETL frameworks. Experience with data modeling, metadata management, ontology development, and master data concepts. Familiarity with data lakes, lakehouses, data warehouses, and knowledge graph concepts. Understanding of AI/ML data requirements Required Skills: Biological Sciences, Biopharmaceutical Industry, Biotechnology, Cross-Functional Leadership, Data Engineering, Data Modeling, Digital Transformation Initiatives, Downstream Process Development, Engineering Standards, Laboratory Informatics, Manufacturing Quality Control, Manufacturing Scale-Up, Manufacturing Systems, Metadata Management, Pharmaceutical Sciences, Predictive Analytics, Regulatory Compliance Preferred Skills: Current Employees apply HERE Current Contingent Workers apply HERE US and Puerto Rico Residents Only: Our company is committed to inclusion, ensuring that candidates can engage in a hiring process that exhibits their true capabilities. Please click here if you need an accommodation during the application or hiring process. As an Equal Employment Opportunity Employer, we provide equal opportunities to all employees and applicants for employment and prohibit discrimination on the basis of race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or other applicable legally protected characteristics. As a federal contractor, we comply with all affirmative action requirements for protected veterans and individuals with disabilities. For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit: EEOC Know Your Rights EEOC GINA Supplement​ We are proud to be a company that embraces the value of bringing together, talented, and committed people with diverse experiences, pe