Software Engineer (Data)
$60,000–$70,000 year
On-siteLondon, England, United Kingdom
Job Summary
Design, build, and ship client ETL pipelines end-to-end, transforming raw source data into validated mappings against the FIRE regulatory standard. Maintain and lead improvements to the shared ETL framework, including schema validation and the FastAPI service layer. Write efficient, well-tested mapping code with ground-truth regression tests, review peer code, and mentor junior engineers on Python best practices. Collaborate with QA, product, and client-facing teams to resolve ambiguity in data specs and ship reliable features. Requires 3-5 years of Python experience, FastAPI proficiency, and strong ETL/data-mapping skills. Based in London with flexible working; salary £60,000 to £70,000. No visa sponsorship available.
Required Qualifications
- 3-5 years of professional experience building production software in Python
- Experience with FastAPI (or a similar web framework)
- Strong understanding of ETL / data-mapping patterns — transforming inconsistent, real-world source data (e.g. CSV extracts) into a normalised target schema, including handling defaults, missing fields, and enumerated value mappings
- Hands-on experience with structured data validation (e.g. JSON Schema), and comfort treating a formal schema as a contract between systems
- Professional experience with Docker, including multi-stage builds
- Solid understanding of RESTful API design
- Solid grasp of Git and standard version-control workflows in a team environment, including working with git submodules
- Solid understanding of data structures, algorithms, and system design principles
- Comfort working in a strictly typed, linted Python codebase (type hints, static type checking, enforced formatting/linting standards)
- Must be available to come into the London office when required
- Some travel may be required
Desired Qualifications
- Experience or interest in the financial services / regulatory reporting domain (loans, deposits, securities, liquidity, credit risk, capital)
- Professional experience processing large volumes of data (complex data pipelines), particularly maintaining many similar-but-distinct integrations rather than a single monolithic application
- Experience contributing to or maintaining an open, schema-driven data standard
- Hands-on experience with DuckDB or another embedded/columnar analytical engine
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