Data Engineer

Lawrence Livermore National Laboratory is hiring a Data Engineer to build and modernize systems that make mission-critical scientific and engineering data discoverable, reusable, and available for analysis. The role develops data pipelines, metadata and schemas, APIs, data quality practices, and tools while partnering with technical teams to improve data management and sharing.

Responsibilities

• Design, build, test, and maintain automated pipelines that ingest, transform, validate, and publish structured and unstructured scientific or engineering data
• Partner with scientists and engineers to understand their workflows and translate their needs into data requirements, schemas, metadata, APIs, and reusable data products
• Develop and apply data quality, lineage, governance, and metadata practices that make data easier to discover, understand, and reuse
• Build interfaces, APIs, dashboards, or other tools that allow technical users to explore data and incorporate it into analysis workflows
• Apply DataOps and software engineering practices - including version control, automated testing, CI/CD, documentation, and containerization - to improve reliability and reproducibility
• Evaluate data platforms, storage technologies, and commercial or open-source tools, and recommend solutions based on mission needs, security constraints, maintainability, and cost
• Communicate data architectures, tradeoffs, risks, and progress clearly to technical and nontechnical stakeholders
• Collaborate across application, platform, data governance, and infrastructure teams to establish shared standards and integration approaches
• Perform other duties as assigned
• Independently define and deliver solutions to complex, ambiguous data management problems spanning multiple projects, organizations, or scientific disciplines
• Set technical direction, evaluate architectural tradeoffs, and provide technical leadership or mentorship for data engineering efforts
• Advise management, customers, and senior technical personnel on data strategy, platform selection, and implementation approaches

Preferred

• Broad experience with workflow orchestration tools such as Airflow, Prefect, or Dagster
• Comprehensive experience with scientific, engineering, manufacturing, national security, or other regulated data environments
• Proficient knowledge of S3-compatible object storage, lakehouse architectures, or distributed data platforms
• Broad experience developing scientific visualizations or dashboards using tools such as Plotly, Streamlit, Tableau, or similar technologies
• Comprehensive knowledge of data architectures supporting semantic search, machine learning, knowledge graphs, or AI-enabled applications
• Workflow orchestration
• Airflow
• Prefect
• Dagster
• Scientific data environments
• Engineering data environments
• Manufacturing data environments
• National security data environments
• Regulated data environments
• S3-compatible object storage
• Lakehouse architectures
• Distributed data platforms
• Scientific visualization
• Dashboard development
• Plotly
• Streamlit
• Tableau
• Semantic search architectures
• Machine learning architectures
• Knowledge graph architectures
• AI-enabled application architectures

Benefits

• Employee referral bonus of $1500
• Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.
• Included in 2026 Best Places to Work by Glassdoor!
• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (depending on project needs)

Company

• Applied science and engineering laboratory solving national security challenges
• Livermore, California, USA
• http://www.llnl.gov/
• Founded in 1952
• 5001-10000 employees

Qualifications

• Ability to obtain and maintain a U.S. Department of Energy Q-level security clearance, which requires U.S. citizenship
• Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical field, or an equivalent combination of education and relevant experience
• Significant experience using Python or another programming language to process, transform, analyze, or validate data
• Broad experience developing data pipelines for structured or unstructured data and working with formats such as JSON, YAML, Parquet, CSV, or XML
• Comprehensive experience with data modeling, schema design, metadata, data quality, or data lineage
• Broad experience using collaborative software development practices such as Git, code review, automated testing, or documentation, with familiarity with CI/CD or containerization concepts
• Comprehensive experience integrating systems through REST APIs, databases, object storage, file-based exchange, or similar mechanisms
• Proficient and demonstrated experience working with customers or subject matter experts to define requirements and deliver technical solutions
• Proficient written and verbal communication skills necessary to explain technical concepts, architectures, and tradeoffs to a range of audiences
• Advanced experience independently architecting and delivering complex data pipelines, platforms, or data products
• Advanced experience analyzing complex scientific, technical, manufacturing, or business workflows and translating them into scalable data solutions
• Significant experience providing technical direction, influencing stakeholders, and recommending systems, services, or process improvements
• Broad experience with workflow orchestration tools such as Airflow, Prefect, or Dagster
• Comprehensive experience with scientific, engineering, manufacturing, national security, or other regulated data environments
• Proficient knowledge of S3-compatible object storage, lakehouse architectures, or distributed data platforms
• Broad experience developing scientific visualizations or dashboards using tools such as Plotly, Streamlit, Tableau, or similar technologies
• Comprehensive knowledge of data architectures supporting semantic search, machine learning, knowledge graphs, or AI-enabled applications

Required skills

• Automated Testing, CI/CD, DOE Q-Level Security Clearance, Data Modeling and Schema Design, Data Pipeline Development, Data Quality and Lineage, Git, JSON, YAML, Parquet, CSV, and XML, Metadata Management, Python, REST APIs, Stakeholder Requirements Gathering, Workflow Orchestration, Workflow Orchestration Airflow, Workflow Orchestration Dagster, Workflow Orchestration Prefect