Senior Specialist, Data Engineering and Analytics

MSD•Upper Gwynedd Township, PA

8d•$117,000 - $184,200•Hybrid

About The Position

Within Small Molecule Science & Technology (SM S&T), the Senior Specialist, Data Engineering and Analytics supports the execution of data and analytics priorities through delivery of high-quality data engineering, data preparation, and analytics solutions. This individual contributor works across SM S&T, Digital Manufacturing Division, Digital CMC, MPI, IT, and Global Pharmaceutical Operations to improve access to manufacturing and process data, support analytics deployment, and enable broader use of digital and analytical capabilities. The role requires technical strength, attention to detail, and the ability to translate defined business needs into reliable, scalable, and compliant data and analytics outputs. The position reports to the Director of Data Engineering and Analytics. The role is based in a hybrid work arrangement, with the primary location in Rahway, New Jersey. Travel to manufacturing sites may be required, estimated at less than 10% of time. The position is a regular, first-shift role. The listed base salary range for the primary location is $117,000 to $184,200, and the successful candidate will be eligible for an annual bonus and long-term incentive, if applicable.

Requirements

Degree (BSc/BEng/Master’s) or higher in Engineering, Pharmaceutical Sciences, Data Science, Information Technology, Computer Science, or a related technical discipline.
Five years of required experience with a qualifying degree; three years with a Master’s degree.
Relevant experience in data engineering, analytics, manufacturing systems, technical operations, or related technical roles.
Working knowledge of manufacturing, process, or operational data in pharmaceutical, biotech, or similarly regulated environments.
Experience with data preparation, transformation, pipeline development, analytics tools, dashboards, or reporting solutions.
Strong analytical, problem-solving, and collaboration skills.
Ability to work effectively across business and technical teams in a matrixed environment.
Demonstrated ability to manage multiple assignments and deliver high-quality work with appropriate guidance.

Nice To Haves

Experience with platforms such as DataLynx, SEEQ, MPI, MES, historians, or similar technologies.

Responsibilities

Execute data engineering and analytics deliverables in support of SM S&T priorities and business needs.
Build, maintain, and enhance data pipelines, data transformations, curated datasets, and analytical outputs.
Support delivery of dashboards, reporting, and analytical tools that improve visibility, monitoring, and decision support.
Investigate data issues and partner with relevant teams to improve data quality, completeness, and usability.
Partner with scientists, engineers, manufacturing users, and digital teams to clarify requirements and support solution delivery.
Translate defined business questions into data requirements, logic, and fit-for-purpose outputs.
Support testing, implementation, and refinement of analytics and data solutions based on user needs and feedback.
Contribute to effective communication and coordination across matrixed teams.
Improve accessibility and usability of manufacturing and process data through preparation, organization, and contextualization of datasets.
Support efforts that enable end users to apply data more effectively in investigations, process monitoring, and routine decision-making.
Document data structures, business logic, and technical approaches to improve sustainability and reuse.
Support deployment and user adoption of analytics tools, data assets, and reporting capabilities.
Provide technical support, user guidance, and knowledge sharing where appropriate.
Identify opportunities to standardize analytical methods, data structures, and ways of working.
Contribute to continuous improvement efforts that strengthen efficiency, scalability, and user experience.
Manage assigned deliverables with attention to timelines, dependencies, and quality expectations.
Escalate risks, issues, and blockers promptly and contribute to resolution planning.
Ensure work is performed consistently with quality, compliance, and documentation expectations in a regulated environment.