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Data Engineer
Job in
Greater London, London, Greater London, W1B, England, UK
Listed on 2026-10-19
Listing for:
SGG Research
Full Time
position Listed on 2026-10-19
Job specializations:
-
Software Development
Python
Job Description & How to Apply Below
- 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.
- 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.
- 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.
- Financial market data, corporate actions, trading calendars, quantitative research or backtesting.
- Text processing, NLP, speaker attribution or entity resolution for structured transcripts.
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