Data Engineer

Data EngineerVitol 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.Vitol'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.Our 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.Job DescriptionThe roleWe 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.This 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.Python 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.The 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.You 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.What You Will DoBuild, test, and operate production data pipelines in Python on our modern pipeline framework, orchestrated with PrefectBuild and maintain warehouse models in Snowflake using dbt — clean, tested, documented, and cost-awareMigrate legacy pipelines and Oracle-based components onto current frameworks and standards, retiring technical debt as you goWork 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 knowAcquire data from external sources — vendor APIs, files, feeds, and web sources — and land it reliablyImplement data quality, freshness, and reconciliation checks so problems surface before users find themUse AWS data services where our platform patterns call for themSupport what you ship — monitoring and alerting on your components, and investigating when something breaksContribute to making platform data AI-ready, and work with the catalog and steward teams so what you build is documented, classified, and findableEngage directly with analysts and desk users to check that what you are building solves the actual problemQualifications3+ years of hands-on data engineering experience building and operating production data pipelinesStrong Python — clean, tested, maintainable code, not scripts that happen to run. Fluency with pandas and the wider data-handling ecosystemSQL depth — you can model, query, and tune, and you know what makes a query expensive before you run itSnowflake or a comparable cloud warehouse — dimensional modeling, performance tuning, and cost-aware design. dbt experience is a strong plusBreadth 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 matchPipeline orchestration experience — Prefect preferred; Airflow, Dagster, or similar consideredSound engineering fundamentals — object-oriented design, design patterns, testing, code review, and version control as habits rather than requirementsWorking knowledge of AWS data services and how to compose them into something reliableA build-to-operate mindset — monitoring, alerting, and failure recovery are part of how you design, not something added laterClear communication — you can explain a technical trade-off to an analyst, and turn a vague request into the right set of questionsTerraform, Docker, or API development (FastAPI, Flask) exposure is welcome — we use all threeExperience in financial services, commodities, or energy trading data is a plus; the data volume, latency requirements, and stakes are realAdditional InformationWhat Success Looks Like in Year OneYou own several production data components outright and they run reliably with minimal interventionWarehouse models you built in Snowflake are in active use by analysts and downstream applicationsA meaningful share of the legacy pipelines you inherited have been migrated onto the current framework and standardsData quality and freshness checks you implemented are catching issues before users report themYou build to the right platform pattern without being told which one applies — and you say so when none of them fitAnalysts on your projects come to you directly, and trust what you deliverComprehensive benefit coverage includes:MedicalDentalVisionPaid Vacation401k with company contributionsLife insuranceShort-term & Long-term disabilityAll your information will be kept confidential according to EEO guidelines.