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Data Engineer

Vitol
Houston, USA
Full-time
Mid · 3+ years experience
Discovered 4 weeks ago
airflowawsdbtdockerfastapiflask

Full Job Posting

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
  • 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
  • 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.
  • 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.
  • Learn more about us here.

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

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