How to apply for Data Engineer

About Argonne National Laboratory

Argonne National Laboratory is a research institution focused on climate solutions and sustainable technologies. The data engineer role supports enterprise data systems that enable reporting, analytics, and automation across the laboratory. Working here means contributing to data infrastructure that supports scientific and operational decision-making.

About the role

This is a senior data engineer position at the Professional Technical 4 level. You will design and build scalable data pipelines, data models, and enterprise data solutions. You will also help shape the architecture of the enterprise data platform and explore AI-assisted approaches to data engineering. The role combines hands-on engineering with technical leadership and stakeholder collaboration.

A typical day

A typical day might involve designing or reviewing data pipelines, meeting with stakeholders to understand data requirements, and writing code to build or improve data models. You could also spend time exploring AI tools to automate data quality checks or transformation tasks. The role includes both hands-on engineering and collaboration with architects, analysts, and other engineers.

Who Argonne National Laboratory is looking for

• Has experience designing and building scalable data pipelines, integrations, and data models for enterprise use.
• Can translate complex business needs from stakeholders into effective data solutions.
• Is proficient in software engineering best practices such as testing, source control, code review, and CI/CD.
• Has worked with cloud data platforms and enterprise data warehouses, and is interested in applying AI to data engineering tasks.

Tips for this application

• Highlight specific projects where you designed data pipelines or data models that supported reporting, analytics, or automation at scale.
• Show experience with cloud data platforms (e.g., AWS, Azure, GCP) and enterprise data warehouse technologies in your resume.
• Include examples of how you applied software engineering best practices like CI/CD, testing, and code review in data engineering work.
• If you have used AI to improve data modeling, transformation, quality, or development, describe it concretely in your application.
• Mention any experience working with stakeholders to translate business needs into technical data solutions, as this is a key responsibility.

What to cover in your cover letter

['Your experience designing and building enterprise data solutions that support reporting, analytics, and automation.', 'A specific example of how you translated a complex business need into a scalable data solution.', 'Your familiarity with software engineering best practices in data engineering, such as testing, CI/CD, and code review.', 'Any experience or interest in applying AI to data engineering tasks, such as improving data quality or automation.']

Research before applying

• Read about Argonne National Laboratory's work in climate solutions and sustainable technologies to understand the context of their data needs.
• Look into Argonne's enterprise data platform or any public information about their data engineering stack and tools.
• Find out if Argonne has published anything about AI in data engineering or their data modernization efforts.
• Check if there are any recent news or projects about Argonne's data infrastructure or analytics initiatives.

Likely interview topics

Based on the job description, expect questions about:

• Walk through a data pipeline you designed and built. What were the requirements, and how did you ensure scalability?
• How do you approach data modeling for enterprise reporting and analytics? Give an example.
• Describe a time you worked with stakeholders to translate a business need into a data solution. What was the outcome?
• What software engineering best practices do you apply in data engineering, and how?
• How have you used or explored AI in data engineering? What was the result?

Common mistakes to avoid

• Focusing only on technical skills without showing how you collaborated with stakeholders or translated business needs.
• Listing tools without demonstrating depth in data pipeline design, data modeling, or cloud data platforms.
• Ignoring the AI aspect of the role; not mentioning any experience or interest in applying AI to data engineering.