Data Engineer

UL Research Institutes is seeking a Data Engineer to support Materials Discovery through the design and evolution of data pipelines, data models, and data platforms. Based in the Skokie, IL office, this onsite role focuses on enabling analytics and machine learning by turning diverse source data into reliable, governed datasets for research and operational use.

In this position, you will own key parts of MDRI’s data platforms, define data standards, and help integrate data from laboratory systems, databases, APIs, and external providers. The work combines software-minded engineering with data quality, reproducibility, and practical machine learning enablement.

What you will do

• Execute architecture and technical implementation of MDRI’s data platforms, including trade-offs across scalability, performance, cost, security, and reliability.
• Define and enforce standards and best practices for data modeling, pipeline design, documentation, data quality, and reproducibility, including 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 while maintaining accuracy, completeness, reproducibility, and timeliness.
• Evaluate and recommend modern data technologies and patterns, including cloud-native services, orchestration frameworks, and feature-ready datasets, aligned to current and future Materials Discovery needs.
• Lead integration of disparate data sources into unified, high-quality datasets and support data governance, security, and compliance with institutional standards and applicable regulations.
• Maintain comprehensive documentation, including contributions 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 and justify modeling techniques, perform exploratory data analysis and feature engineering, and develop 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.
• Serve as a technical partner to researchers, analysts, and leadership on data architecture, analytical feasibility, and strategic trade-offs.
• Troubleshoot complex data and model issues across development and production environments.
• Perform other duties as assigned.

Requirements

• 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 including Azure, AWS, or Google Cloud, and associated data and analytics services.
• Familiarity with infrastructure-as-code and containerization tools such as Terraform, Docker, and Kubernetes.
• Experience with data integration, orchestration tools, and distributed processing frameworks such as Apache Spark, Azure Databricks, and 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.

Salary and location

• Location: Skokie, IL (onsite)
• Salary: USD 81,456 - 112,002 per year
• Pay type: Salary

Education and experience

• 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.

Technology stack

• SQL, Python, pandas, NumPy, scikit-learn, PyTorch, TensorFlow
• Azure, AWS, Google Cloud
• Terraform, Docker, Kubernetes
• Apache Spark, Azure Databricks, Azure Data Factory
• ETL/ELT

Benefits

• Bonus compensation eligibility
• Comprehensive medical, dental, vision, and life insurance plans
• 401k matching structure of up to 5% of eligible pay
• Additional 4% into your retirement saving fund after your first year of continuous employment
• Flexible working arrangements may be available depending on role
• Paid time off including vacation, holiday, sick, and volunteer days