Theia Insights builds foundational financial intelligence products, including industry classification, knowledge graphs and factor risk models, for institutional investors. We serve some of the largest asset managers, hedge funds, index providers and sell-side banks.
As an engineer on the Data Products team you'll own the pipelines that ingest NLP and financial data from public equities around the world to produce the Theia Insights Industry Classification (TIIC) and the datasets behind our Thematic Factor Risk Models (TFM).
The Data Products team owns the data that underpins everything we sell. It's a small, senior group that values correctness and reproducibility over volume, and it sits close to the product leads who shape the methodology. We value durability and good judgement over familiarity with the flashiest tools.
What you’ll do
Build and maintain pipelines that classify global public equities across our five-level taxonomy, sector, industry, sub-industry, major theme and micro theme, by extracting information from filings and web content, and assigning thematic exposures based on this information.
Run large-scale NLP and LLM inference (entity extraction, classification, knowledge graph construction) over company documents, with cost- and throughput-aware batch execution.
Ingest market data and publish datasets to external distributors.
Own schema and contract evolution for datasets with real downstream consumers.
Work with economists and engineers to turn modelling decisions into reliable production data.
Essential
Strong production Python.
Datasets in pandas and Parquet/Arrow, plus an analytical engine, e.g. DuckDB, or a warehouse such as Snowflake.
Orchestrated batch pipelines you've operated, not just written: Dagster or Airflow, S3-based data flows, and a habit of testing outputs for correctness rather than only for exceptions.
Applied ML in production: embeddings and semantic similarity, clustering, or operationalising models (not necessarily training from scratch).
AWS fluency and CI/CD discipline.
Nice to have
Practical LLM engineering: prompting, batch inference, and cost and throughput trade-offs across providers.
SageMaker, Bedrock, or comparable managed ML tooling.
Financial and equities domain knowledge: classification taxonomies, factor models, index construction. Valuable but learnable.
Infrastructure as code (AWS CDK or Terraform) and Docker.
Experience we’re looking for
We care more about what you’ve owned than years on a CV. If you’ve built a pipeline that runs on a schedule against real volume, and you were the person who got paged when it broke, you’re in scope. More senior candidates will typically have made the cost and throughput trade-offs, and set the standard for how a team tests data quality.
Competitive salary plus share options
25 working days holiday, plus Spanish public holidays
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