Data Engineer – ML-Driven Data Platforms for Discovery

Lucas James Talent Partners; Skokie (IL); On-site; USD 110,000 - 165,000; Full time

Job summary

UL Research Institutes in Skokie, IL is seeking a Data Engineer to join the Materials Discovery team. You will build and maintain data pipelines, data models, and data platforms that enable analytics and machine learning across the institute.

Collaborate with data scientists to define standards, design ETL/ELT workflows from diverse sources, and contribute to data governance and comprehensive documentation.

Qualifications

• Experience owning and evolving data platforms end-to-end.
• Strong proficiency in SQL and Python; ML libraries.
• Experience with cloud platforms (Azure, AWS, Google Cloud).
• Familiarity with Docker, Kubernetes and IaC.

Responsibilities

• Architect and implement MDRI data platforms with scalable pipelines.
• Define data models, pipelines, documentation, data quality.
• Design and evolve ETL/ELT pipelines from diverse sources.
• Assess and adopt modern data technologies and patterns.
• Lead data integration and governance across datasets.
• Collaborate with researchers to determine data and modeling approaches.
• Experiment with modeling techniques; develop and evaluate ML models.
• Document assumptions and hand off validated models.
• Advise researchers and leadership on data architecture and strategy.
• Troubleshoot data and model issues across environments.

Skills

SQL

Python

Data analysis

ML libraries

Azure

AWS

Google Cloud

Terraform

Docker

Kubernetes

Apache Spark

Azure Databricks

Azure Data Factory

Education

Bachelor’s degree in Computer Science, IT, Data Science, or Engineering

Job description

UL Research Institutes in Skokie, IL is seeking a Data Engineer to join the Materials Discovery team. You will build and maintain data pipelines, data models, and data platforms that enable analytics and machine learning across the institute.

Collaborate with data scientists to define standards, design ETL/ELT workflows from diverse sources, and contribute to data governance and comprehensive documentation.