Data Engineer - Materials Discovery Research Institute

UL Research Institutes • Skokie, IL

1d • Onsite

About The Position

The Data Engineer role within Materials Discovery focuses on building, maintaining, and supporting reliable data pipelines, data models, and data platforms that enable analytics and machine learning across the institute. The position applies core data engineering practices while contributing selectively to applied data science tasks such as problem definition, data sourcing and preparation, exploratory analysis, and model development. Working closely with data scientists, researchers, senior technical team members, this role plays a key part in onboarding and integrating Engineering‑generated data into Materials Discovery data infrastructure. The position contributes to architectural and tooling decisions and helps ensure data is well‑structured, accessible, and fit for downstream analytical and modeling workflows.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, or equivalent combination of education and experience.
• Minimum 4 years of experience in a data engineering, analytics engineering, or closely related role.
• Demonstrated experience supporting or developing machine learning, statistical
• Demonstrated experience owning and evolving data platforms or systems end‑to‑end.
• Strong proficiency in SQL and Python, including experience with data analysis and machine learning libraries (e.g., pandas, NumPy, scikit‑learn, PyTorch, TensorFlow).
• Experience with cloud platforms such as Azure, AWS, or Google Cloud and associated data and analytics services.
• Familiarity with infrastructure‑as‑code and containerization (e.g., Terraform, Docker, Kubernetes).
• Experience with data integration, orchestration tools, and distributed processing frameworks (e.g., Apache Spark, Azure Databricks, Azure Data Factory).
• Solid understanding of machine learning fundamentals, feature engineering, evaluation techniques, and experiment reproducibility.
• Knowledge of data governance, security, privacy, and compliance best practices.
• Strong communication, problem-solving, and technical judgment skills, with the ability to adapt messaging for technical and non‑technical audiences.

Responsibilities

• Execute the architecture and technical implementation of MDRI’s data platforms, making informed trade‑off decisions related to scalability, performance, cost, security, and reliability.
• Define and enforce standards and best practices for data modeling, pipeline design, documentation, data quality, and reproducibility, including implementation of automated data quality checks and validation processes.
• Design, build, and evolve data architectures and ETL/ELT pipelines to collect, process, and store data from diverse sources (e.g., laboratory systems, databases, APIs, and external data providers), ensuring data accuracy, completeness, reproducibility, and timeliness.
• Evaluate, recommend, and introduce modern data technologies and patterns (e.g., cloud‑native services, orchestration frameworks, feature‑ready datasets) aligned with Materials Discovery’s current and future needs while proactively addressing system limitations, scaling risks, and performance bottlenecks
• Lead integration of disparate data sources into unified, high-quality datasets and ensure data governance, security, and compliance with institutional standards and applicable regulations.
• Maintain comprehensive documentation and contribute to data dictionaries and metadata repositories to support long‑term sustainability.
• Collaborate with researchers and stakeholders to determine effective data and modeling approaches for research, operational, and business challenges.
• Assess, select, and justify modeling techniques; perform exploratory data analysis and feature engineering; and develop, train, and evaluate machine learning and statistical models to establish feasibility, baselines, and data requirements.
• Clearly document assumptions, inputs, outputs, limitations, and evaluation results, and hand off validated models, feature sets, and documentation for deployment and operationalization.
• Act as a technical partner and advisor to researchers, analysts, and leadership on data architecture, analytical feasibility, and strategic trade-offs, while influencing cross-functional technical direction and planning discussions
• Assist with troubleshooting complex data and model issues across development and production environments.
• Perform other duties as assigned.

Benefits

• bonus compensation
• comprehensive medical, dental, vision, and life insurance plans
• 401k matching structure of up to 5% of eligible pay
• an additional 4% into your retirement saving fund after your first year of continuous employment
• flexible working arrangements
• paid time off, including vacation, holiday, sick, and volunteer days

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level