Senior Specialist: Data Engineer for Upstream Biologics

Merck•Boston, MA

12d•$117,000 - $184,200•Hybrid

About The Position

We are seeking a Senior Specialist, 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 the teams developing them, and in this Senior Specialist, Data Engineer role, the successful candidate will design, build, and maintain data pipelines that capture, curate, and deliver experimental and process data from Upstream Biologics Process Development teams – including cell culture, bioreactor operations, media and feed optimization, and high throughput process development – into downstream digital initiatives. The engineer will contribute directly to ontology alignment efforts, support the integration of new data streams, and deliver analysis-ready datasets to support process characterization models, scale-up predictions, and multivariate analytics that de-risk and accelerate biologics manufacturing from bench through commercial scale. 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

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 version control systems (Git/GitHub) and collaborative software development practices.
Ability to work in a team environment with cross-functional interactions
Motivated to learn new skills, willingness to take on new challenges, and scientific curiosity

Nice To Haves

Experience with upstream biologics unit operations, particularly cell culture (fed-batch, perfusion), bioreactor operation and scale-up, media and feed optimization, or harvest and clarification.
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).
Evidence of cross-functional collaborations spanning laboratory, manufacturing, modeling, and digital teams.

Responsibilities

Build and maintain robust, scalable data pipelines that ingest experimental and process data from upstream biologics source systems
Deliver analysis-ready datasets to support upstream digital initiatives, including process characterization models, scale-up predictions, multivariate analytics, and high throughput process development workflows.
Map instrument outputs and experimental results ensuring ontology alignment and interoperability across upstream data sources.
Develop and maintain data visualizations, dashboards, and reports that enable upstream scientists to explore process data across runs, molecules, and scales.
Support system of record standards by ensuring consistent data entry practices.
Identify and flag data quality issues, gaps in metadata, and inconsistencies across source systems, contributing to continuous improvement of upstream data capture practices.
Collaborate with upstream process development scientists, analytical scientists, and engineers to understand evolving data needs and translate them into pipeline requirements.
Coordinate with adjacent domain engineers to ensure seamless data handoffs at domain boundaries.
Maintain and version all pipeline code in GitHub, following team standards for code review, documentation, and deployment.
Demonstrate excellent interpersonal, communication, and collaboration skills
Embrace and model our core values of inclusion, including fostering a supportive culture where all can thrive.
Collaborate effectively in a dynamic, integrated, and multidisciplinary team environment.