Scientific Data Engineering Lead

Job Description

Job Description

THE ROLE
We are seeking an exceptional Scientific Data Engineering Lead to join our digital R&D capability. In this role, you will partner directly with our scientific community to translate complex domain knowledge into robust, traceable data structures, ensuring high-quality data is captured at the source to power internal scientific data and AI platforms.

You will be a key collaborator with critical stakeholders across our R&D and Digital teams, requiring travel to our Headquarters in New York City and Operating Companies (OpCos), both domestically and internationally. You will act as the vital translator between physical laboratory/manufacturing realities and digital data ecosystems, evaluating architecture trade-offs to turn materials and chemicals data into scalable, strategic company assets.

RESPONSIBILITIES
This role blends data architecture, pipeline engineering, and applied ML; day-to-day focus is expected to weigh toward pipeline engineering and governance, with the remainder as ongoing but secondary responsibilities.

The Scientific Data Engineering Lead will:

• Partner with scientists and engineers across OpCos to define data semantics, architecture and quality requirements to support scientific and business workflows.
• Work with OpCos to define and implement data governance and quality standards at the source of ingestion (e.g., LIMS, MES, CoA, Historian, etc.).
• Select optimal data models (relational, NoSQL, graph) and schemas for scientific datasets.
• Build and optimize cloud-based ETL/ELT pipelines using medallion architecture.
• Help curate and augment legacy datasets for machine-readability and AI readiness.
• Perform exploratory data analysis to monitor health and identify anomalies.
• Develop prototype dashboards and web apps for business partner consumption.
• Deploy baseline ML models into production, owning the pipeline end-to-end using DevOps and MLOps best practices, in partnership with our platform and CloudOps team.

THE PERSON

Required Skills

• Proficiency in Python, SQL and IaC for data manipulation and modeling.
• Familiarity with the google cloud platform (GCP) and Azure Cloud and IaC tools and techniques.
• Expertise in diverse data modeling frameworks (e.g., graph, relational, object-oriented).
• Knowledge of materials, chemicals, or manufacturing data types.
• Experience with cloud data lake architectures and delta shares.
• Experience building and optimizing cloud-based ELT/ETL pipelines using medallion architecture (e.g., Databricks/Delta Lake or equivalent), with transformation logic in dbt or PySpark and orchestration via Airflow, Dagster, or a cloud-native scheduler.
• Foundational machine learning, Git version control, and CI/CD skills.
• Familiarity with time series data.
• Strategic thinker with strong cross-disciplinary communication skills across technical, IT/OT and scientific disciplines.
• Technical degree in Chemistry, Engineering, or Computer Science.

Required Experience

• 5+ years of experience in data engineering, data architecture, or applied ML, ideally within scientific, industrial, or manufacturing data environments.
• Demonstrated experience translating abstract domain-expert concepts (from scientists or engineers) into functional, deployed data models.
• Proven track record of engineering data pipelines, cleaning historical data, and building rapid-consumption prototypes in Python or Javascript.
• Demonstrated experience in facilitating change and aligning technical standards across a decentralized or multi-location organization.

Preferred Qualifications

• Experience working directly at the intersection of operational R&D and enterprise digital transformation.
• An advanced degree in Engineering or UX/UI is highly preferred.
• Experience working directly at the intersection of operational R&D and enterprise digital transformation.
• Familiarity with data governance tools or automated data-quality validation frameworks at the point of ingestion.
• Prior expertise communicating data-architecture trade-offs directly to senior management and diverse operational teams.