Data Engineer

Data Engineer 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.

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 Description The role

We 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 Do

Build, test, and operate production data pipelines in Python on our modern pipeline framework, orchestrated with Prefect

Build and maintain warehouse models in Snowflake using dbt — clean, tested, documented, and cost-aware

Migrate legacy pipelines and Oracle-based components onto current frameworks and standards, retiring technical debt as you go

Work 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

Acquire data from external sources — vendor APIs, files, feeds, and web sources — and land it reliably

Implement data quality, freshness, and reconciliation checks so problems surface before users find them

Use AWS data services where our platform patterns call for them

Support what you ship — monitoring and alerting on your components, and investigating when something breaks

Contribute to making platform data AI-ready, and work with the catalog and steward teams so what you build is documented, classified, and findable

Engage directly with analysts and desk users to check that what you are building solves the actual problem

Qualifications 3+ years of hands-on data engineering experience building and operating production data pipelines

Strong Python — clean, tested, maintainable code, not scripts that happen to run. Fluency with pandas and the wider data-handling ecosystem

SQL depth — you can model, query, and tune, and you know what makes a query expensive before you run it

Snowflake or a comparable cloud warehouse — dimensional modeling, performance tuning, and cost-aware design. dbt experience is a strong plus

Breadth 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

Pipeline orchestration experience — Prefect preferred; Airflow, Dagster, or similar considered

Sound engineering fundamentals — object-oriented design, design patterns, testing, code review, and version control as habits rather than requirements

Working knowledge of AWS data services and how to compose them into something reliable

A build-to-operate mindset — monitoring, alerting, and failure recovery are part of how you design, not something added later

Clear communication — you can explain a technical trade-off to an analyst, and turn a vague request into the right set of questions

Terraform, Docker, or API development (FastAPI, Flask) exposure is welcome — we use all three

Experience in financial services, commodities, or energy trading data is a plus; the data volume, latency requirements, and stakes are real

Additional Information What Success Looks Like in Year One

You own several production data components outright and they run reliably with minimal intervention

Warehouse models you built in Snowflake are in active use by analysts and downstream applications

A meaningful share of the legacy pipelines you inherited have been migrated onto the current framework and standards

Data quality and freshness checks you implemented are catching issues before users report them

You build to the right platform pattern without being told which one applies — and you say so when none of them fit

Analysts on your projects come to you directly, and trust what you deliver

Comprehensive benefit coverage includes:

Medical

Dental

Vision

Paid Vacation

401k with company contributions

Life insurance

Short-term & Long-term disability

All your information will be kept confidential according to EEO guidelines.