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 such as pandas, NumPy, scikit-learn, PyTorch, and 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, including Terraform, Docker, or Kubernetes.
Experience with data integration, orchestration tools, and distributed processing frameworks such as Apache Spark, Azure Databricks, or 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.
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 or statistical models.
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, including laboratory systems, databases, APIs, and external data providers, ensuring data accuracy, completeness, reproducibility, and timeliness.
Evaluate, recommend, and introduce modern data technologies and patterns, such as cloud-native services, orchestration frameworks, and feature-ready datasets, aligned with Materials Discovery’s needs while 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.
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.
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 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.
What you’ll experience working at UL Research Institutes: We have pursued our mission of working for a safer, more secure, and sustainable world for nearly 130 years, embedding conscientious stewardship into everything we do.
People: Our people make us special. You’ll work with a diverse team of experts respected for their independence and transparency and build a network, because our approach is collaborative. We collaborate across disciplines, organizations, and geographies to build the global scientific response that today’s global challenges require.
Interesting work: Every day is different for us here. We see what’s on the horizon and use our expertise to build the foundations of a safer future. You’ll have the opportunity to push the boundaries of human understanding as part of a team working to advance the public good.
Grow and achieve: We learn, work, and grow together through targeted development, reward, and recognition programs.
Values. Four core values guide our work: collaboration, respect, integrity, and beneficence. By living our values, we inspire the trust essential to fulfilling our mission and foster the partnerships that enable us to pursue a beneficent future in which we all can thrive.
Total Rewards: All employees at UL Research Institutes are eligible for bonus compensation. We offer comprehensive medical, dental, vision, and life insurance plans and a generous 401k matching structure of up to 5% of eligible pay. Moreover, we invest an additional 4% into your retirement saving fund after your first year of continuous employment. Depending on your role, you may be able to discuss flexible working arrangements with your manager. We also provide employees with paid time off, including vacation, holiday, sick, and volunteer days.
What makes you a great fit:
While no one candidate will embody every quality, the successful candidate will bring many of the following professional competencies and personal attributes:
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.
Professional education and experience requirements for the role include:
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
About UL Research Institutes and UL Standards & Engagement
UL Research Institutes and UL Standards & Engagement are nonprofit organizations dedicated to advancing safety science research through the discovery and application of scientific knowledge. We conduct rigorous independent research and analyze safety data, convene experts worldwide to address risks, share knowledge through safety education and public outreach initiatives, and develop standards to guide safe commercialization of evolving technologies. We foster communities of safety, from grassroots initiatives for neighborhoods to summits of world leaders. Our organization employs collaborative and scientific approaches with partners and stakeholders to drive innovation and progress toward improving safety, security, and sustainability, ultimately enhancing societal well-being.