Data & BI Engineer – Commodity Markets Analytics, CTRM Platform

• Own the data and BI layer of the CTRM platform end to end
• Build ingestion pipelines from internal systems and external market data providers, including scheduling, retries, backfills and vendor schema changes
• Model commodity data covering prices and curves, cargoes and flows, deals, positions, costs, units, calendars, pricing periods and reference data
• Build dashboards and reports for price analysis, spreads, cargo flows and margin, including crack spread monitoring, cargo tracking, refinery margin analysis and storage economics
• Implement data quality, lineage and reconciliation, including freshness checks, price-series gap detection, duplicate-cargo detection and trusted-source reconciliation
• Establish agreed definitions across trading, operations and finance and document reproducible calculations
• Translate trader requirements into working analytics and iterate with end users
• Evaluate and recommend new data sources, tools and integrations as the platform grows
• Work directly with traders, operations, commercial staff, the senior data scientist and founders

• 4+ years as a Data Engineer, BI Developer, Analytics Engineer or in a comparable role, with ownership of production pipelines and reporting
• Strong SQL
• Python for pipelines, automation and analysis, including pandas and SQLAlchemy or similar
• Practical data modelling experience, including dimensional modelling, star schemas, and dbt or equivalent
• Hands-on experience with a BI tool such as Power BI, Tableau, Metabase or Superset, from requirements through to a relied-upon dashboard
• Commodity markets experience, ideally crude oil and refined products; knowledge of price assessments, forward curves, crack spreads and cargo nominations
• Strong, demonstrable interest in commodity markets may partly compensate for experience; zero exposure will not
• Experience working seriously with coding agents and strong verification habits, including row-count and freshness checks, reconciliation, and transformation tests
• Comfortable working directly with traders and commercial staff and explaining data issues in their terms
• English sufficient to present work and defend design decisions
• Professional proficiency in Russian
• Nice to have: market data provider and API experience with Kpler, Platts, OPIS, Argus or Bloomberg
• Nice to have: cloud data warehouses such as Snowflake, BigQuery or Databricks
• Nice to have: workflow orchestration with Airflow, Prefect, Dagster or similar
• Nice to have: experience in an energy trading company, commodity house or data-heavy financial environment, including CTRM/ETRM exposure

• Ownership of the data and BI layer, and room to shape the architecture and tooling
• Direct access to experienced commodity traders, the founders and decision-makers; short feedback loops
• Licences and access to leading market data platforms, including Kpler, Platts and OPIS
• Generous AI tooling budget — frontier models are a working expense, not a perk
• Competitive compensation, discussed individually