Data Engineer - Materials Discovery Research Institute

Job Description

We have an exciting opportunity for a Data Engineer at UL Research Institutes, based in our Skokie, Illinois office. This is an onsite opportunity.

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.

UL Research Institutes:

At UL Research Institutes (ULRI), we expand the boundaries of safety science to create a more secure and sustainable world. For more than a century, we have studied the unintended consequences of innovation, designed solutions to mitigate risk and shared our findings with academia, scientists, manufacturers, and policymakers across industries. We identify critical safety and sustainability issues, asking the tough questions because we believe a safer world begins with knowledge.

Build a safer, more secure, and sustainable future with us. Join us and work with Materials Discovery teams who conduct the research required to produce that knowledge and put into practice.

Materials Discovery Research Institute:

The Materials Discovery Research Institute (MDRI) works to develop and deploy new materials with the potential to address current global safety challenges. Pursuing materials that will help produce transformational safety breakthroughs, MDRI harnesses the power of advanced computing and high-throughput experimental methods to create innovative materials that will produce resilience for a sustainable future and protect individual and societal health.

We focus on today’s critical challenges, working to create new and better materials that will support renewable energy and environmental sustainability. Among our top priorities is research into materials capable of carbon capture and energy storage, with an eye toward reducing the adverse impacts of humanity’s reliance upon fossil fuel resources and enabling a transition to renewable energy sources.

Above all, our research builds on our commitment to a safer, more sustainable future.

What you’ll learn and achieve:

As the you Data Engineer, will play a key role in the rapid growth of UL as you:

• 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 valid