Data Engineer
Builds and owns the market/economic/news data pipelines and bronze/silver/gold architecture powering Macro Hive's AI models, quant signals and client tools. Core stack is Python, SQL, and a cloud data platform (Azure, Databricks, Delta Lake, Snowflake), with emphasis on time-series data quality.
- Compensation
- Not disclosed
- Location
- London, United Kingdom
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Strong Python and SQL
- Production ETL/ELT experience
- Cloud data stack: Azure, Databricks, Delta Lake, Snowflake or similar
- Time-series or complex-data experience
- Data quality, monitoring and audit awareness
- Ability to turn messy data into clean model-ready datasets
Nice to have
- Experience with ticker level data using e.g. kdb+
Full description
Macro Hive (BGC Group) is a leading independent provider of global macro strategy. We combine best-in-class human and artificial intelligence to provide institutional investors with market-leading investment opportunities. Our experts leverage large language models, such as those that power chatbots like ChatGPT, to develop innovative, data-driven solutions to complex financial problems and to build state-of-the-art models in search of alpha signals across asset classes.
Data Engineer — Markets & AI Data Platform
Mission:
Build the data backbone for AI, quant models and client tools.
Owns:
- Market, economic, research, news, internal and Fenics/BGC data pipelines
- Bronze/silver/gold data architecture
- Instrument, ticker, country, client, source and theme metadata
- APIs/tables for agents, dashboards and quant models
- Data lineage, entitlements, permissions and monitoring
Must have:
- Strong Python and SQL
- Production ETL/ELT experience
- Cloud data stack: Azure, Databricks, Delta Lake, Snowflake or similar
- Time-series or complex-data experience
- Data quality, monitoring and audit awareness
- Ability to turn messy data into clean model-ready datasets
Nice to have
- Experience with ticker level data using for example kdb+