Data & BI Engineer — Commodity Markets Analytics (CTRM Platform)

What the work looks like

The platform we are building exists so that anyone involved in a deal can see prices, flows, positions and margin across the chain, at any moment, on agreed and versioned definitions. Your part is the data underneath that and the decision-support layer on top: market intelligence from third-party providers joined to our own trading and operations data, across crude oil and refined products.

A dashboard here is only as good as the number behind it. Traders act on spreads and margins within minutes; if a price series is stale, a unit is wrong or a cargo is counted twice, someone loses money before anyone notices.

Scope

* Ingestion pipelines (ELT/ETL) from internal systems and external market data providers — Kpler, Platts / S&P Global, OPIS, Argus, Bloomberg — including scheduling, retries, backfills and vendor schema changes
* A central commodity data model: prices and curves, cargoes and flows, deals, positions, costs — with consistent units, calendars, pricing periods and reference data
* Dashboards and reports for price analysis, spreads, cargo flows and margin — for example crack spread monitoring, cargo tracking, refinery margin analysis and storage economics
* Data quality, lineage and reconciliation as part of the job, not an afterthought: freshness checks, gaps in price series, duplicate cargoes, reconciliation to a source someone already trusts
* Single agreed definitions where trading, operations and finance disagree, and documentation that lets someone else rebuild a figure
* Turning loosely stated trader requirements into working analytics quickly, iterating with the people who will use it
* Evaluating and recommending new data sources, tools and integrations as the platform grows

Requirements

* 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 (pandas, SQLAlchemy or similar)
* Data modelling in practice — dimensional modelling, star schemas, dbt or equivalent
* Hands-on experience with a BI tool such as Power BI, Tableau, Metabase or Superset, taken from requirement to a dashboard people rely on
* Commodity markets experience, ideally crude oil and refined products. You know what a price assessment, a forward curve, a crack spread and a cargo nomination are, and why the same cargo can appear differently in two data sources. Strong, demonstrable interest can partly compensate; zero exposure will not
* Working with coding agents, seriously. Agents write most of the code here. Bad agent output in data pipelines fails silently: a join fans out rows, a timezone shift moves a price to the wrong day, a unit conversion is applied twice. We hire for verification habits, not speed — row-count and freshness checks, reconciliation against a number someone already trusts, tests on the transformations that matter
* Comfortable working directly with traders and commercial staff, and explaining a data issue in their terms
* English sufficient to present work and defend design decisions
* Professional proficiency in Russian

Nice to have

* Hands-on experience with market data providers (Kpler, Platts, OPIS, Argus, Bloomberg) and their APIs
* Cloud data warehouses (Snowflake, BigQuery, Databricks or similar)
* Workflow orchestration (Airflow, Prefect, Dagster or similar)
* Experience in an energy trading company, commodity house or data-heavy financial environment; exposure to CTRM/ETRM systems

What we offer

* 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

How to apply

Share your CV and an answer to any of the questions below, at your choice. Applications without answers are not reviewed.

1. Traders say the gasoline crack spread on the dashboard “looks wrong” on some days but not others. The inputs come from two price providers and an internal curve. How would you investigate, what would you check first, and what would you change so it does not happen again?
2. Give a real case where two systems or two data providers reported different numbers for the same price, cargo, volume or position. What were the figures, why did they differ, which one did the business treat as truth, and what did you build or change?
3. Show a real prompt or task brief you gave an agent for a pipeline, transformation or dashboard, and what it produced. Then one case where agent output looked right and was wrong: what was wrong, how you caught it, what check or test you added.
4. Which market data or trading systems have you personally pulled data from — Kpler, Platts, OPIS, Argus, Bloomberg, a CTRM/ETRM, ERP? Pick one and tell us something about its data that only someone who has used it would know.