{"id":3355153,"url":"https://alion.io/job/sigma-software-group-senior-data-engineer-6","title":"Senior Data Engineer","company":{"id":6266,"name":"Sigma Software Group","domain":"sigma.software","url":"https://alion.io/company/sigma-software-group","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":["PL"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":63000,"max_usd":95000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":71},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon S3","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"Great Expectations","optional":false},{"name":"IAM","optional":false},{"name":"MLFlow","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-10-27T15:59:42Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-10-29T02:58:42Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Join an ambitious deep-tech initiative shaping the future of clean energy. We are looking for a Senior Data Engineer to design and scale cloud-native data platforms for a pioneering fusion energy company working on the world’s first commercial laser fusion power plant.\nIn this role, you will work on greenfield data engineering initiatives, build near-real-time pipelines, and modernize large-scale data infrastructure using Databricks and AWS technologies.\nWe at Sigma Software create opportunities to work on innovative global projects where technology directly impacts the future of science and sustainability. This role offers the chance to contribute to breakthrough research while driving engineering excellence, automation, and AI adoption within the data platform ecosystem.\nCUSTOMER\nOur Customer is a German-American deep-tech company building the world’s first commercial laser fusion power plant. The company develops direct-drive laser fusion technology and is commercializing the 2021 breakthrough at Lawrence Livermore, where scientists achieved fusion ignition for the first time.\n Design and build scalable cloud-native data platforms from greenfield to production\nDesign and implement near-real-time ingestion pipelines using event-driven patterns\nDefine and enforce data platform standards including Data Lake/Lakehouse principles, medallion architecture, and data contracts\nRefactor, optimize, and modernize Spark and PySpark scripts for performance and maintainability\nIntroduce best practices for code quality, testing, and CI/CD across data pipelines\nDrive adoption of AI tooling and agentic workflows within the data engineering team\nEnsure data quality, observability, and reliability across pipelines and platforms\nDesign and deliver self-service tooling and microservices that abstract platform complexity\nCollaborate directly with the Customer’s scientific and engineering teams\nMonitor platform performance, reliability, and cloud infrastructure costs\n 5+ years of experience in data engineering\n3+ years of production experience with Databricks and Delta Lake\nStrong expertise in medallion architecture and event-driven asynchronous pipeline design\nAdvanced proficiency in Python, PySpark, and SQL\nDeep hands-on experience with Databricks, including Unity Catalog, Lakeflow Jobs/Workflows, and job optimization\nStrong AWS expertise including S3, IAM, KMS, and services such as AWS Batch, ECR, EventBridge, Lambda, or API Gateway\nExperience with infrastructure as code and CI/CD using Terraform and Databricks Asset Bundles\nPractical experience with Git workflows, pull request reviews, and automated testing\nExperience configuring secure CI pipelines using OIDC or service principals\nPractical MLflow experience including experiment tracking, model registration, and reproducibility practices\nStrong understanding of data quality, lineage, provenance, schema contracts, and versioning\nExperience with data quality frameworks such as Great Expectations, Soda, pyDeequ, or Databricks Labs DQX\nExperience with monitoring, observability, and cloud cost optimization\nUpper-Intermediate English level or higher\nWILL BE A PLUS\nExperience packaging workloads in Docker containers and running batch jobs on AWS\nFamiliarity with experiment optimization tools such as scikit-optimize, BoTorch, Ax, and scipy\nExperience building feature sets and train/test splits that avoid data leakage\nExperience with regulated or high-security environments including data residency requirements\nExperience disabling or restricting AI features in secured environments\n PERSONAL PROFILE\nStrong analytical mindset\nOwnership and accountability\nAbility to communicate complex technical concepts clearly\nProactive approach to problem-solving\nAttention to scalability and maintainability\nCollaborative and team-oriented 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