Data Engineer

• Location to be agreed, with a remote / hybrid way of working. Location options include London, Hamburg (Germany) and Canggu (Bali), with office use arranged with the team.
• In-person meetings, workshops and events are arranged according to project needs. Location, timing and any travel requirements are agreed with the team.

What you will work on

• Build and maintain Python pipelines for structured transcripts, market data and daily factor datasets, including incremental updates, corrections and historical backfills.
• Model participant and company relationships; implement graph algorithms for traversal, connectivity, centrality and network evolution across observation windows.
• Design PostgreSQL schemas, migrations, indexes and efficient queries. Work with graph databases and choose appropriate graph and relational representations for each workload.
• Implement identity resolution, provenance, versioning and point-in-time joins so research uses only information available at the relevant decision time.
• Operate authenticated data APIs and scheduled jobs with monitoring, retries, idempotency, rate limits and clear failure handling.
• Develop validation checks and automated tests for data quality, historical reconstruction and reproducible portfolio evaluation.

What you should bring

• Strong Python and SQL skills, including practical experience with data processing libraries such as pandas or Polars and working with large tabular datasets.
• Hands-on PostgreSQL experience: data modelling, query planning, indexing, transactions and performance troubleshooting.
• Experience with graph algorithms and a graph database such as Neo4j, including the ability to explain when a graph database is useful and when relational storage is sufficient.
• Experience building API integrations and production data pipelines, with careful handling of timestamps, missing values, duplicate records and changing source data.
• Confidence with Git, Linux, Docker, automated testing and deployment workflows; an understanding of access control and secure handling of credentials.
• Clear technical communication and the ability to turn a research requirement into maintainable, documented software.

Useful additional experience

• Financial market data, corporate actions, trading calendars, quantitative research or backtesting.
• Text processing, NLP, speaker attribution or entity resolution for structured transcripts.