Senior Data Engineer

Responsibilities: Develop and implement tools and analyses to improve the efficiency and consistency of information communicated across the global Commercial organization Own data quality in practice: completeness, mapping and transformation checks, duplication and history validation, and the defect backlog that comes with new sources Build and maintain curated silver and gold data products for commercial domains, including outages, ship tracking, plant capacity and runs, market prices, and balances Implement standardized, reusable transformations so calculations are defined once, documented, and consistent across every desk and every downstream tool Lead ad-hoc projects and analysis to support Market Analysts, Strategists and Traders Lead information gathering processes and development of presentation packages as needed Work to improve existing tools and processes used by the Commercial organization Share knowledge freely and improving the team’s development capabilities Support ad-hoc requests from Senior Management, various Commercial groups, the Chief Economist’s Office, and Investor Relations QualificationsLegally authorized to work in the United States Bachelor’s degree or higher Required Skills1 or more years in the finance or energy industry 5 years of experience with SQL and Python Experience with Databricks, Data Factory, Azure DevOps, and GitPreferred SkillsHands-on experience with Databricks, Unity Catalog, medallion architecture, or comparable lakehouse tooling Bachelor’s degree in MIS, Engineering or Business 5 or more years in the finance or energy industry Experience of Web application development (Flask/Django/Dash etc.) Experience of at least one of the following: Commercial trading businesses Commodity Markets Refining/Petrochemical Process Willingness and ability to support internal customers in business requirements High degree of initiative - works proactively to anticipate needs. Strong ability to manage multiple projects simultaneously Familiarity with dbt or similar transformation frameworks, orchestration tools, and data testing libraries Experience translating spreadsheet-based business logic into maintained pipelines Real instinct for data quality: you reconcile, question anomalies, and validate before you publish Genuine curiosity about the business behind the data, and the willingness to learn refinery capacity, outages, logistics, and trading fundamentals rather than staying purely technical