Data Engineer

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title as heading. No Job ID etc. Remove boilerplate. We need to parse the posting and extract relevant content. Let's identify sections: - About the role: description of role, maybe first paragraph(s). The posting starts: "Lucas James Talent Partners is recruiting on behalf of UL Research Institutes. We have an exciting opportunity for a Data Engineer at UL Research Institutes, based in our Skokie, Illinois office. 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." That's about the role. - Responsibilities: likely the "What you’ll learn and achieve:" bullet list (the long list). That includes many responsibilities. - Requirements / Qualifications: "What makes you a great fit:" list of competencies and personal attributes. Also "Professional education and experience requirements for the role include:" list. - Skills: maybe combine with requirements? Could separate. - Benefits: "Total Rewards:" paragraph about bonus, medical, dental, vision, life insurance, 401k matching, additional 4% retirement, flexible working arrangements, paid time off. - Pay: Not specified. No pay info. So we should NOT include Pay section. - Schedule: Not specified. No schedule info. So omit. We need to avoid filler like "commensurate with experience" etc. Not present. We need to output only HTML fragment, no markdown fences. We'll structure:

About the role

... (the paragraph(s) about role)

Responsibilities

• ... each bullet from "What you’ll learn and achieve:" list.

Qualifications

• ... from "What makes you a great fit:" list.

Maybe also separate "Education and Experience" as part of Qualifications. We could have

Education and Experience

but maybe combine. But we need to follow instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can use Requirements and Qualifications separate if content exists. Let's see: "Professional education and experience requirements for the role include:" is clearly Requirements. "What makes you a great fit:" is more like Qualifications/Skills. Could treat as Qualifications. We could also have Skills section: list of technical skills (SQL, Python, cloud platforms, etc.) from the "What makes you a great fit:" list. But we can incorporate into Qualifications. Better to split: Requirements (education/experience), Qualifications (competencies/attributes), Skills (technical skills). However we must avoid thin sections; but we have enough content. Let's extract: About the role paragraph(s): The first two paragraphs up to "The position contributes to architectural and tooling decisions and helps ensure data is well‑structured, accessible, and fit for downstream analytical and modeling workflows." We could also include the paragraph about UL Research Institutes description? That's more about company; maybe not needed but could be part of About the role? The instruction: preserve ALL substantive information; do not invent or summarize away detail. So we should include the description of ULRI and MDRI? That is substantive about the organization. Could be part of About the role or maybe a separate section like "About the company". But headings list does not include About the company; only About the role etc. We could incorporate into About the role paragraph. Thus About the role will include: recruitment line, role description, ULRI description, MDRI description, maybe also "What you’ll learn and achieve:" is responsibilities. Let's extract ULRI description: "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." MDRI description: "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." We should keep these. Now Responsibilities: list from "What you’ll learn and achieve:" includes many items each starting with a verb. Let's extract each bullet: - 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. We need to ensure each bullet is a

. Now Requirements: "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" It cuts off? The text: "Demonstrated experience supporting or developing machine learning, statistical" maybe incomplete due to copy. We'll keep as is. Now Qualifications/Skills: "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." We could split into Qualifications (competencies) and Skills (technical). But we can just put all under Qualifications. Benefits: "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." Now we need to ensure we don't include boilerplate like "About UL Research Institutes and UL Standards & Engagement" etc. That appears after the qualifications. That is boilerplate about organization; maybe we should exclude as it's not specific to role? It's still substantive but maybe considered boilerplate? The instruction: Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The "About UL Research Institutes and UL Standards & Engagement" is not exactly those but could be considered organizational description; but we already included ULRI and MDRI description earlier. This later paragraph seems repetitive. Might be considered boilerplate; we can drop it to avoid duplication. Thus we will not include that later paragraph. Now construct HTML. We need to ensure we don't include any markdown fences. Let's craft:

About the role

Lucas James Talent Partners is recruiting on behalf of UL Research Institutes. We have an exciting opportunity for a Data Engineer at UL Research Institutes, based in our Skokie, Illinois office. 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