Associate Director, Data Engineer: DSCS Digital Data Strategy

Merck•Boston, MA

2d•$129,000 - $203,100•Hybrid

About The Position

We are seeking an Associate Director, Data Engineer to join our Digital Insights team within the Development Sciences and Clinical Supply (DSCS) Digital Technologies organization. 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. The tools that we develop are as diverse as the teams developing them, and in this Associate Director, Data Engineer role, the successful candidate will serve as a domain owner for data engineering in the biologics space — designing, building, and governing data pipelines that capture, curate, and deliver experimental and process data, including chromatography, filtration, and purification, into digital initiatives spanning process characterization, data lineage, and multivariate analytics. This role and the work being done to build this data product will be associated with efforts to establish DSCS Digital Data Strategy. This engineer will help establish end-to-end data strategy in the biologics space: cataloging all processes, analytics, and systems; enforcing ontology alignment across data sources; aligning proactively with colleagues as new automated workflows create new data streams; and ensuring seamless data handoffs to adjacent domains. The pipelines and data products built here will directly enable colleagues across Digital Insights to deploy modeling, optimization, and decision-support tools that de-risk and accelerate biologics drug substance manufacturing from bench through commercial scale and across multiple manufacturing sites. This role sits within the Digital Insights team which is working to digitally enable a decision engine for DSCS, by innovating solutions through strong partnership in the domains of Data Science, Data Analysis, Informatics, Multi-Omics, Predictive Science, and Data Engineering.

Requirements

Ph.D. in Chemical Engineering, Biochemical Engineering, Biochemistry, Computer Science, Data Science, Engineering, Chemistry, Biology, Pharmaceutical Sciences, or a closely-related field with at least 3 years of industrial/pharmaceutical or relevant experience.
M.S. in Chemical Engineering, Biochemical Engineering, Biochemistry, Computer Science, Data Science, Engineering, Chemistry, Biology, Pharmaceutical Sciences, or a closely-related field with at least 5 years of industrial/pharmaceutical or relevant experience.
B.S. in Chemical Engineering, Biochemical Engineering, Biochemistry, Computer Science, Data Science, Engineering, Chemistry, Biology, Pharmaceutical Sciences, or a closely-related field with at least 7 years of industrial/pharmaceutical or relevant experience.
Proficient in Python and/or R programming. Comfortable working in development environments such as Jupyter, Posit/RStudio, or VS Code.
Solid SQL skills with hands-on experience writing and optimizing queries against relational databases and data warehouses.
Experience with ETL/ELT processes and building data 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 consume and depend on experimental data — sufficient to anticipate modeler needs and deliver appropriately structured datasets.
Experience defining or enforcing data standards, metadata schemas, or ontology mappings in a scientific or pharmaceutical context.
Familiarity with version control systems (Git/GitHub) and collaborative software development practices.
Demonstrated 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.

Nice To Haves

Prior hands-on experience in biologics process development — including chromatography, filtration, purification, or formulation — with a demonstrated transition into a data engineering, data science, or computational role.
Experience with data pipeline and analytics platforms such as Databricks, including notebook-based development, workflow orchestration, and Delta Lake.
Experience with data visualization tools (Streamlit, Shiny, PowerBI, Spotfire, or Tableau) for building scientist-facing dashboards and exploratory data applications.
Familiarity with ontology frameworks or standardized data models (e.g., Allotrope Simple Model, ISA-88, OPC-UA) and experience mapping instrument data to structured schemas.
Understanding of Design of Experiments (DoE) methodologies and process characterization study designs — sufficient to structure data for statistical analysis of critical process parameters (CPPs) and critical quality attributes (CQAs).
Experience with data lineage concepts and building traceability across experimental systems, materials, and manufacturing steps.
Knowledge of regulatory expectations relevant to biologics process development, process characterization, and process validation (e.g., ICH Q8-Q12, process validation lifecycle, comparability studies).
Experience working alongside or supporting lab automation teams, including integrating data from newly automated laboratory workflows.
Experience with cross-site data integration, including harmonizing data from multiple manufacturing facilities with different systems and conventions.
Evidence of cross-functional collaborations spanning laboratory, manufacturing, modeling, and digital teams.

Responsibilities

Serve as a domain owner in the biologics data engineering space, maintaining full awareness of all digital projects, data sources, systems, and data flows within the domain.
Inform work being done to establish 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 systems, electronic lab notebooks, and analytical instruments.
Deliver analysis-ready datasets to support digital initiatives, including process characterization models, data lineage tracking, multivariate analytics, and cross-site manufacturing connectivity.
Define and enforce data standards, metadata schemas, and ontology mappings that make biologics data interoperable and readily consumable by modeling and optimization workflows.
Align proactively with automation colleagues, anticipating when new or modified automated workflows create new data streams that require pipeline development and ontology mapping.
Own and govern system of record standards for biologics, ensuring consistent configuration and data entry practices across experiments, molecules, and sites.
Catalog all processes, analytical methods, instruments, and digital systems within the biologics domain, creating a comprehensive map of the data landscape.
Develop and maintain data visualizations, dashboards, and reports that enable scientists to explore process data across runs, molecules, scales, and manufacturing sites.
Influence digital data strategy for biologics by identifying opportunities to improve data capture practices at the source and reduce friction between experimentation and modeling.
Mentor and guide supporting data engineers working across modalities, ensuring alignment with domain strategy and ontology governance.
Maintain and version all pipeline code in GitHub, following 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 digital data strategy for the domain.
Demonstrate excellent interpersonal, communication, and collaboration skills.
Embrace and model our core values of diversity and inclusion, including fostering a supportive culture where all can thrive.
Collaborate effectively in a dynamic, integrated, and multidisciplinary team environment.