{"id":3454306,"url":"https://alion.io/job/luxoft-senior-data-scientist","title":"Senior Data Scientist","company":{"id":171185,"name":"Luxoft","domain":"luxoft.com","url":"https://alion.io/company/luxoft","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career sitemap","truth_index":{"grade":"A","score":88,"open_postings":8,"ghost_share":0,"stale_share":0.625,"repost_share":0.125,"time_to_fill_p50_days":20,"computed_at":"2026-11-03T05:54:40Z"}},"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":["Ukraine"],"countries":["UA"],"hiring_countries":["UA"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":30000,"max_usd":79000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"Node JS","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"TypeScript","optional":false},{"name":"dbt","optional":true},{"name":"JavaScript","optional":true},{"name":"LLM","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Ruby","optional":true},{"name":"Ruby on Rails","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-22T14:06:38Z","employer_posted_date":null,"last_verified_at":"2026-10-22T14:06:38Z","board_verified":false,"closed_at":null,"days_open":12,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":12},"description":"Project Description:\n\nEnvana is an energy solutions provider. Our AI-enabled platform supports operations, tracking and decision-\n\nmaking to optimize large-scale renewable and traditional energy portfolios. We're delivering a key client MVP\n\nin February 2027, and the project continues into 2028. Our stack: Ruby on Rails, Node.js/TypeScript and\n\nPython services, PostgreSQL, Snowflake and AWS. We're looking for an autonomous data engineer to\n\nstrengthen our data foundation through good modeling, clean schemas, reliability and automation.\n\nResponsibilities:\n\nWork closely with product teams to help define and execute technical criteria\n\nArchitect and maintain robust data ingestion pipelines that feed both our application and analytics\n\nDesign well-modeled, layered tables with clean schemas that turn raw data into analytics-ready data\n\nHelp onboard client data from a variety of sources, such as Databricks via Delta Sharing, and integrate it\n\nwith our existing services and workflows\n\nMake pipelines reliable and automated: data quality tests, monitoring, safe reprocessing and schema\n\nchange handling\n\nBuild a documented, well-tested data foundation that serves the app, analytics and future AI use cases\n\nMandatory Skills Description:\n\nMid-to-senior data engineer who has owned the design of production data pipelines end to end, working\n\nas an autonomous IC\n\nDesigning and building production ingestion pipelines from APIs, files and shared-data sources, using a\n\nworkflow orchestrator (any tool, e.g. Airflow, Dagster or Step Functions)\n\nProduction experience with a cloud data warehouse (Snowflake preferred)\n\nStrong SQL, plus production experience in Python (preferred) or Node.js/TypeScript\n\nAWS or another enterprise cloud platform\n\nAI-native development: daily use of agentic AI coding tools, and experience scaling shared AI workflows\n\nacross a team and into the development lifecycle, including CI/CD\n\nStrong English and communication skills\n\nNice-to-Have Skills Description:\n\ndbt\n\nPreparing data for AI/LLM use (e.g. semantic layers, metadata, retrieval-ready datasets)\n\nInfrastructure as code (e.g. Terraform)\n\nExposure to energy, utilities or sustainability data\n\nLanguages:\n\nEnglish: B2 Upper 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