Help organisations transform complexity into measurable outcomes by combining technology, data, intelligence, and human decision-making.
Build and maintain robust ingestion pipelines for real-time and historical market data, including exchange feeds, vendor sources (Bloomberg, LSEG/Refinitiv, ICE) and internal sources.
Normalise and standardise heterogeneous feeds into consistent internal schemas, so downstream consumers never have to care where the data came from.
Integrate market and reference data with the client's internal systems, such as research libraries, backtesting infrastructure, risk, PnL, and security master.
Own the data plumbing that decisions rely on: Corporate actions, Symbology mapping, Point-in-time accuracy, Gap detection & reconciliation.
Model and store time-series and tick data efficiently in ClickHouse and/or kdb+/q, balancing query performance, storage cost, and ingestion throughput.
Build monitoring, alerting, and data quality frameworks to proactively detect issues.
Requirements
+5 years working as a Data Engineer
Strong experience building production-grade data pipelines where reliability is critical
Proven exposure to financial market data environments (real-time feeds, formats, vendor data)
Experience working with large-scale datasets
Professional proficiency in English
Strong Python skills for data engineering, orchestration, and tooling (pandas, polars)