Fundamentals | Private Credit | Lakehouse | Data Products | AI
London (hybrid)
This is a data product ownership, lakehouse, data modelling, data governance, AI-led discovery role. Product Managers or Data Product Owners with significant experience owning versioned, SLA-bound financial datasets consumed by downstream products, APIs and clients. You will be tasked with owning what a data product is in a data curation platform organisation: schema, versioning, service levels, entity resolution, quality and coverage rules, and domain content.
THE ROLE:
- Lakehouse: Databricks, Snowflake, Iceberg, Delta Lake, Unity Catalog
- Data modelling: conceptual, logical, physical; joins, profiling, entity resolution
- Fundamental financial data: as-reported, standardised, restatements, fiscal periods, XBRL, identifiers, entity master
- Private credit data: loan-level, covenants, borrowers, facilities, sponsors, lenders, valuation marks
- Discovery by prototype: SQL, Python, notebooks, AI-assisted tooling
- Data DevOps: CI/CD for data, testing, release
Own the data product: definition, schema, versioning rules, service levels, quality and coverage rules, and consumption. Own the domain: how financial statement data is produced and consumed (Seat A), or how private credit data is sourced from documents and used (Seat B). Own discovery: prototype the dataset, query or view before engineering commits.
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