- Location: 2 days a month in Sofia Office
Not many data engineering roles come with this kind of scope. This is a price-reporting agency with a history stretching back to 1865 - sitting at the centre of global commodities markets across agriculture, metals, energy, and forest products. More than 700 people across the UK, US, Asia, Europe, and beyond. The data produced here moves markets.
The Data Platform team has built the entire modern data stack from scratch and is still very much in build mode. You'd be joining a team working on genuinely complex data problems - new pipelines in the carbon and analytics divisions, expanding monetizable data assets, and embedding AI tooling (Anthropic Claude and Snowflake Cortex) into the day-to-day engineering workflow. This isn't a maintenance role; there's real greenfield work here.
What They're Looking For:
- Snowflake as a cloud data warehouse, including Snowpark for Python-based transformations
- dbt (Data Build Tool) and Jinja templating
- Python data pipelines - Pandas, GreatExpectations, custom connectors
- Dagster for orchestration and observability
What You'll Work With:
- Advanced SQL across multiple flavours - Snowflake SQL and MSSQL
- ELT/ETL tooling - Fivetran, Airbyte, or similar connector-based platforms
- GitHub and GitHub Actions for CI/CD and version control
- Kubernetes and containerised on-demand development environments
- Anthropic Claude and GitHub Copilot for AI-assisted engineering
- Snowflake Cortex AI for in-platform intelligence
- Azure cloud services (preferred, not mandatory)
- Solid data modelling approaches - Star Schema, Kimball/Inmon, 3NF
Nice to Haves:
- Data Vault 2.0 experience
- Kubernetes and Infrastructure as Code (Terraform or Pulumi)
- Snowflake Cortex AI and native app development
- Event streaming - Kafka, Azure Service Bus, or similar
- SnowPro certification
- Exposure to Salesforce or Marketo
Why Join / Projects:
The team built the whole platform themselves and has real ownership of where it goes next. You'd be landing with two clear project tracks: net-new pipeline builds across the carbon and analytics teams, and ongoing evolution of existing platform infrastructure. The carbon data space is a fast-growing area for them, so there's scope to get in early on something that's still taking shape.
AI isn't a buzzword here - they've embedded it into the engineering workflow properly, and they expect new joiners to embrace that rather than work around it. If you've already got a view on how to use Claude or Copilot to move faster and write better code, that'll land well.
Worth a conversation if this sounds like your kind of environment, apply and I'll give you a call.
Senior Data Engineer | Snowflake, dbt, Dagster, Python | Global Commodities Price-Reporting Agency
