{"id":884565,"url":"https://alion.io/job/vitol-data-engineer","title":"Data Engineer","company":{"id":691744,"name":"Vitol.com","domain":"vitol.com","url":"https://alion.io/company/vitol-2","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"A","score":95,"open_postings":5,"ghost_share":0,"stale_share":0.2,"repost_share":0,"time_to_fill_p50_days":57,"computed_at":"2026-11-08T05:47:12Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Houston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":92000,"max_usd":176000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":530},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Dagster","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"InfluxDB","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"Redis","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-09-11T21:10:00Z","employer_posted_date":"2026-09-11","last_verified_at":"2026-11-08T05:57:00Z","board_verified":true,"closed_at":null,"days_open":57,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":57},"description":"Vitol is a leader in energy and commodities. Vitol produces, manages and delivers energy and commodities to consumers and industry worldwide. In addition to its primary business of trading, Vitol is invested in infrastructure globally, with $10+billion invested in long-term assets.\nVitol’s customers include national oil companies, multinationals, leading industrial companies and utilities. Founded in Rotterdam in 1966, today Vitol serves its customers from some 40 offices worldwide. Revenues in 2023 were $400bn.\nOur people are our business. Talent is precious to us and we create an environment in which individuals can reach their full potential, unhindered by hierarchy. Our team comprises more than 65+ nationalities and we are committed to developing and sustaining a diverse work force.\nLearn more about us here.\nThe role\nWe are looking for a Data Engineer to build and run the pipelines and data models behind our core data platform - the centralized data layer that serves global energy traders and analysts in real time.\nThis is a hands-on delivery role. You will write the Python that moves market and fundamental data from source through to the analysts, applications, and AI systems that consume it, and you will support what you ship.\nPython is the core skill, but it is not the whole job. We are standing up a Snowflake warehouse, migrating legacy pipelines onto a Prefect-based framework, and consolidating how the platform stores and exposes data. The work reaches across more of the stack than pipeline code alone.\nThe platform is polyglot by design: streaming, caching, timeseries, relational, and warehouse technologies each doing what they are good at. You will build to the established patterns across that stack, and we expect you to understand why each technology sits where it does.\nYou will work alongside senior engineers who set those patterns, and directly with the analysts and desk users your work serves. The scope grows as you do - this is a role you can build a platform career from.\nWhat You Will Do\nBuild, test, and operate production data pipelines in Python on our modern pipeline framework, orchestrated with Prefect\nBuild and maintain warehouse models in Snowflake using dbt - clean, tested, documented, and cost-aware\nMigrate legacy pipelines and Oracle-based components onto current frameworks and standards, retiring technical debt as you go\nWork across the platform stack - Kafka, Redis, InfluxDB, Oracle, and Snowflake - building to the established pattern for each rather than reaching for the tool you already know\nAcquire data from external sources - vendor APIs, files, feeds, and web sources - and land it reliably\nImplement data quality, freshness, and reconciliation checks so problems surface before users find them\nUse AWS data services where our platform patterns call for them\nSupport what you ship - monitoring and alerting on your components, and investigating when something breaks\nContribute to making platform data AI-ready, and work with the catalog and steward teams so what you build is documented, classified, and findable\nEngage directly with analysts and desk users to check that what you are building solves the actual problem\n3+ years of hands-on data engineering experience building and operating production data pipelines\nStrong Python - clean, tested, maintainable code, not scripts that happen to run. Fluency with pandas and the wider data-handling ecosystem\nSQL depth - you can model, query, and tune, and you know what makes a query expensive before you run it\nSnowflake or a comparable cloud warehouse - dimensional modeling, performance tuning, and cost-aware design. dbt experience is a strong plus\nBreadth across data technologies - streaming, caching, timeseries, relational, and warehouse, with a view on where each belongs. Kafka, Redis, InfluxDB, Oracle, and Snowflake are what we run; comparable exposure matters more than an exact match\nPipeline orchestration experience - Prefect preferred; Airflow, Dagster, or similar considered\nSound engineering fundamentals - object-oriented design, design patterns, testing, code review, and version control as habits rather than requirements\nWorking knowledge of AWS data services and how to compose them into something reliable\nA build-to-operate mindset - monitoring, alerting, and failure recovery are part of how you design, not something added later\nClear communication - you can explain a technical trade-off to an analyst, and turn a vague request into the right set of questions\n Terraform, Docker, or API development (FastAPI, Flask) exposure is welcome - we use all three\nExperience in financial services, commodities, or energy trading data is a plus; the data volume, latency requirements, and stakes are real\nWhat Success Looks Like in Year One\nYou own several production data components outright and they run reliably with minimal intervention\nWarehouse models you built in Snowflake are in active use by analysts and downstream applications\nA meaningful share of the legacy pipelines you inherited have been migrated onto the current framework and standards\nData quality and freshness checks you implemented are catching issues before users report them\nYou build to the right platform pattern without being told which one applies - and you say so when none of them fit\nAnalysts on your projects come to you directly, and trust what you deliver\nComprehensive benefit coverage includes:\nMedical\nDental\nVision\nPaid Vacation\n401k with company contributions\nLife insurance\nShort-term & Long-term disability\nAll your information will be kept confidential according to EEO guidelines.","description_format":"text","description_chars":5623,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":["401k plan","Life insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Fossil Fuels","Oil & Gas"],"lifecycle":[{"event":"open","at":"2026-09-14T03:11:34Z"}],"visa":[{"country":"US","licensed_sponsor":true,"adverts_offering_sponsorship_24m":0,"sanctioned_now":false,"evidence":"H-1B filings in 12 months: 5 · green card filings: 1","filings_12m":5,"filings_prev_12m":10,"green_card_filings_12m":1,"median_offered_wage_usd":150000,"route":null,"cap_exempt":false,"checked_at":"2026-11-07T07:57:00+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":2}],"liveness":{"score":44,"band":"fade","label":"Fading","p_open":1,"p_active":0.796,"p_room":0.55,"age_days":57,"expected_fill_days":57,"reasons":["conf:3","velocity","win:tail"],"computed_at":"2026-11-08T05:47:12Z"},"pay":null,"html_url":"https://alion.io/job/vitol-data-engineer","json_url":"https://alion.io/job/vitol-data-engineer.json","meta":{"generated_at":"2026-11-08T08:30:52Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":18398,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-09T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":691744},"rest":"https://alion.io/mcp/rest/get_company?id=691744"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fvitol-data-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fvitol-data-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fvitol-data-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/vitol-data-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fvitol-data-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":884565}},{"tool":"search_jobs","arguments":{"company":"vitol.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":884565},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}