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Company
F2F Systems is building the internal data infrastructure required to map difficult organic waste streams, treatment capacity, and commercial opportunities across the waste system.
Role
We are looking for a Data Engineer to build the systems that turn fragmented public, commercial, and operational information into structured, decision-useful datasets. The role would involve:
- Designing and maintaining F2F’s internal data architecture
- Ingesting and cleaning large external datasets
- Building pipelines for waste movements and facility data
- Resolving and matching sites and companies across inconsistent sources
- Creating the internal mapping systems used to identify feedstock, infrastructure, and commercial opportunities
You would work across geospatial data, ETL pipelines, APIs, database design, and internal tooling. A core part of the role would be taking poorly structured real-world information and making it reliable enough to support commercial and operational decisions.
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This is not a role for the faint-hearted. F2F is early-stage, priorities will change quickly, and you will be expected to work things out rather than wait for instructions. In return, the right person will have an unusually high level of responsibility from the outset and the opportunity to materially shape the systems on which the company is built.
For an exceptional candidate, there is scope for rapid career progression, significant responsibility, and equity participation, with long-term compensation aligned to the value created.
The Candidate
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We are particularly interested in someone at the beginning of their career who is exceptionally capable, hungry, and wants considerably more responsibility than a conventional role would provide. Age and years of experience matter less to us than raw ability, judgement, pace of learning, and willingness to take ownership.
Experience with Python, SQL, PostgreSQL, geospatial data, APIs, and data pipelines would be particularly relevant. Experience with GIS, Supabase, PostGIS, web scraping, entity resolution, or large public datasets would also be highly preferable.
We would typically expect a First-Class degree in a relevant discipline from a leading university, or evidence of comparable technical ability.
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