Associate Director, Data Engineer: DSCS Digital Data Strategy

Merck is seeking an Associate Director, Data Engineer within the DSCS Digital Data Strategy team, based on site in Boston, MA. The role centers on designing, constructing, and governing biologics data pipelines while shaping an end-to-end data strategy to support modeling, optimization, and decision-making across Digital Insights. This position requires a PhD and at least three years of relevant experience, with a salary range of $129,000 to $203,100 per year.

Responsibilities

• Own the biologics data engineering domain, maintaining broad awareness of digital projects, data sources, systems, and data flows within the domain.
• Contribute to defining and guiding the DSCS Digital Data Strategy.
• Design and implement robust, scalable data pipelines that ingest experimental and process data from biologics source systems including process historians, chromatography equipment, electronic lab notebooks, and analytical instruments.
• Provide analysis-ready datasets to support digital initiatives, covering process characterization models, data lineage, multivariate analytics, and cross-site manufacturing connectivity.
• Establish and enforce data standards, metadata schemas, and ontology mappings to ensure biologics data is interoperable and readily usable by modeling and optimization workflows.
• Collaborate proactively with automation teams to anticipate when new or modified automated workflows generate new data streams requiring pipeline development and ontology mapping.
• Own and govern system-of-record standards for biologics, ensuring consistent configuration and data entry across experiments, molecules, and sites.
• Catalog processes, analytical methods, instruments, and digital systems within the biologics domain to create a comprehensive data-landscape map.
• Create and maintain data visualizations, dashboards, and reports that enable scientists to explore process data across runs, molecules, scales, and manufacturing sites.
• Contribute to the biologics digital data strategy by identifying opportunities to improve data capture at the source and reduce friction between experimentation and modeling.
• Mentor and guide supporting data engineers across modalities, ensuring alignment with domain strategy and ontology governance.
• Maintain and version pipeline code in GitHub, adhering to team standards for code review, documentation, and deployment.
• Build strong partnerships with process development scientists, analytical scientists, and manufacturing teams to gather requirements and shape the domain's digital data strategy.
• Demonstrate excellent interpersonal, communication, and collaboration skills.
• Model and promote diversity and inclusion within the team, fostering a supportive culture where all can thrive.
• Collaborate effectively in a dynamic, integrated, multidisciplinary environment.

Requirements

• Proficiency in Python and/or R, with comfort using development environments such as Jupyter, Posit/RStudio, or VS Code.
• Strong SQL skills with hands-on experience writing and optimizing queries for relational databases and data warehouses.
• Experience building ETL/ELT pipelines in a scientific or pharmaceutical context.
• Familiarity with cloud platforms (AWS, Azure, or GCP) for data storage, processing, and integration.
• Working knowledge of how process models, multivariate analyses, and statistical tools rely on experimental data to anticipate modeler needs and deliver structured datasets.
• Experience defining or enforcing data standards, metadata schemas, or ontology mappings in a scientific or pharmaceutical setting.
• Familiarity with version control systems (Git/GitHub) and collaborative software development practices.
• Proven ability to lead technical initiatives, mentor junior engineers, and influence data strategy across multiple stakeholders.
• Ability to deliver complex solutions under compressed timelines in a dynamic environment.

Technologies

• Python and R
• Jupyter, Posit/RStudio, VS Code
• SQL
• AWS, Azure, GCP
• Databricks, Delta Lake
• Git, GitHub
• Streamlit, Shiny
• Power BI, Spotfire, Tableau
• Allotrope Simple Model, ISA-88, OPC-UA

Benefits

• Medical, dental, and vision coverage for you and your family
• Retirement benefits including a 401(k) plan
• Paid holidays and vacation time
• Sick leave and compassionate time off
• Annual bonus and potential long-term incentives where applicable

Preferred Experience and Skills

• Hands-on biologics process development experience—such as chromatography, filtration, purification, or formulation—and a track record transitioning into data engineering, data science, or computational roles.
• Experience with data pipelines and analytics platforms such as Databricks, including notebook-based development, workflow orchestration, and Delta Lake.
• Proven ability to create scientist-facing dashboards and exploratory data applications using visualization tools (Streamlit, Shiny, Power BI, Spotfire, Tableau).
• Familiarity with ontology frameworks or standardized data models (e.g., Allotrope Simple Model, ISA-88, OPC-UA) and data mapping from instruments to structured schemas.
• Understanding of Design of Experiments and process characterization study designs to structure data for statistical analysis of CPPs and CQAs.
• Experience implementing data lineage and traceability across experimental systems, materials, and manufacturing steps.
• Knowledge of regulatory expectations relevant to biologics process development, characterization, and validation (ICH Q8-Q12, validation lifecycle, comparability studies).
• Experience collaborating with lab automation teams to integrate data from newly automated workflows.
• Experience with cross-site data integration and harmonizing data from multiple manufacturing facilities.
• Evidence of cross-functional collaboration spanning laboratory, manufacturing, modeling, and digital teams.