{"id":2883566,"url":"https://alion.io/job/lawrence-livermore-national-laboratory-data-engineer","title":"Data Engineer","company":{"id":5246,"name":"Lawrence Livermore National Laboratory","domain":"llnl.gov","url":"https://alion.io/company/llnl","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":80,"open_postings":25,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":20,"computed_at":"2026-10-21T05:46:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Livermore, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":146340,"max":185544,"currency":"USD","period":"year","gross":null,"usd_annual":185544},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Git","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Plotly","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Streamlit","optional":false},{"name":"Tableau","optional":false}],"status":"live","first_seen_at":"2026-10-19T20:44:44Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-22T02:41:12Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Join us and make YOUR mark on the World!\nLawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.\nOur mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.\n We have an opening for a Data Engineer to build and modernize data systems that make mission-critical scientific and engineering data discoverable, reusable, and available for analysis. You will partner with scientists, engineers, software developers, and data stewards to understand complex experimental and operational workflows and translate them into reliable data products.\nThe work spans structured and unstructured data, automated pipelines, metadata and schema design, data quality and lineage, APIs, visualization, and integration with modern data platforms. You will have opportunities to improve legacy processes, introduce reusable engineering practices, and help shape how research data is managed and shared within LLNL and across the National Nuclear Security Administration complex.\nThis position is in the Applications, Simulations, and Quality Division within the Computing Directorate, supporting Strategic Deterrence data management programs.\nDepending 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.\nThis position will be filled at either level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities and qualifications will apply if hired at the higher level.\nYou will\nDesign, build, test, and maintain automated pipelines that ingest, transform, validate, and publish structured and unstructured scientific or engineering data.\nPartner with scientists and engineers to understand their workflows and translate their needs into data requirements, schemas, metadata, APIs, and reusable data products.\nDevelop and apply data quality, lineage, governance, and metadata practices that make data easier to discover, understand, and reuse.\nBuild interfaces, APIs, dashboards, or other tools that allow technical users to explore data and incorporate it into analysis workflows.\nApply DataOps and software engineering practices - including version control, automated testing, CI/CD, documentation, and containerization - to improve reliability and reproducibility.\nEvaluate data platforms, storage technologies, and commercial or open-source tools, and recommend solutions based on mission needs, security constraints, maintainability, and cost.\nCommunicate data architectures, tradeoffs, risks, and progress clearly to technical and nontechnical stakeholders.\nCollaborate across application, platform, data governance, and infrastructure teams to establish shared standards and integration approaches.\nPerform other duties as assigned.\nAdditional job responsibilities at the SES.3 level\nIndependently define and deliver solutions to complex, ambiguous data management problems spanning multiple projects, organizations, or scientific disciplines.\nSet technical direction, evaluate architectural tradeoffs, and provide technical leadership or mentorship for data engineering efforts.\nAdvise management, customers, and senior technical personnel on data strategy, platform selection, and implementation approaches.\n Ability to obtain and maintain a U.S. Department of Energy Q-level security clearance, which requires U.S. citizenship.\nBachelor’s degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical field, or an equivalent combination of education and relevant experience.\nSignificant experience using Python or another programming language to process, transform, analyze, or validate data.\nBroad experience developing data pipelines for structured or unstructured data and working with formats such as JSON, YAML, Parquet, CSV, or XML.\nComprehensive experience with data modeling, schema design, metadata, data quality, or data lineage.\nBroad experience using collaborative software development practices such as Git, code review, automated testing, or documentation, with familiarity with CI/CD or containerization concepts.\nComprehensive experience integrating systems through REST APIs, databases, object storage, file-based exchange, or similar mechanisms.\nProficient and demonstrated experience working with customers or subject matter experts to define requirements and deliver technical solutions.\nProficient written and verbal communication skills necessary to explain technical concepts, architectures, and tradeoffs to a range of audiences.\nAdditional qualifications at the SES.3 level\nAdvanced experience independently architecting and delivering complex data pipelines, platforms, or data products.\nAdvanced experience analyzing complex scientific, technical, manufacturing, or business workflows and translating them into scalable data solutions.\nSignificant experience providing technical direction, influencing stakeholders, and recommending systems, services, or process improvements.\nQualifications we desire\nBroad experience with workflow orchestration tools such as Airflow, Prefect, or Dagster.\nComprehensive experience with scientific, engineering, manufacturing, national security, or other regulated data environments.\nProficient knowledge of S3-compatible object storage, lakehouse architectures, or distributed data platforms.\nBroad experience developing scientific visualizations or dashboards using tools such as Plotly, Streamlit, Tableau, or similar technologies.\nComprehensive knowledge of data architectures supporting semantic search, machine learning, knowledge graphs, or AI-enabled applications.\nPay Range\n$146,340 - $185,544 Annually for the SES.2 level\n$175,530 - $222,564 Annually for the SES.3 level\nThis is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.\n All your information will be kept confidential according to EEO guidelines.\nPosition Information\nThis is a Career Indefinite position, open to Lab employees and external candidates.\nWhy Lawrence Livermore National Laboratory?\nIncluded in 2026 Best Places to Work by Glassdoor!\nFlexible Benefits Package\n401(k)\nRelocation Assistance\nEducation Reimbursement Program\nFlexible schedules (*depending on project needs)\nOur values - visit https://www.llnl.gov/inclusion/our-values\nSecurity Clearance\nThis position requires a Department of Energy (DOE) Q-level clearance. If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship.\nPre-Employment Drug Test\nExternal applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.\nWireless and Medical Devices\nPer the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.\nIf you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.\nHow to identify fake job advertisements\nPlease be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under “Find Your Job” of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.\nTo learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf\nEqual Employment Opportunity\nWe are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.\nReasonable Accommodation\nOur goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.\nCalifornia Privacy Notice\nThe California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. 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