Senior Market Data Platform Engineer (Python) | Dynamic Asset Management Leader

• Architect, build, and refine high-throughput pipelines capable of processing large volumes of real-time and historical market data
• Develop resilient, well-structured Python/OOP solutions underpinning tick-data ingestion, normalisation, storage, and replay
• Design and evolve data-lake patterns using Parquet and related columnar formats; introduce Iceberg-style table modelling where beneficial
• Implement cloud-backed processing and storage patterns - leveraging services from AWS, Azure, or GCP for scale, resilience, and cost efficiency
• Improve system performance across latency, throughput, and reliability, ensuring pipelines support fast-moving trading environments
• Work with time-series technologies such as KDB or OneTick to optimise retrieval, querying, and analytics workflows
• Collaborate closely with researchers, traders, and engineering partners to translate data requirements into robust production solutions
• Introduce validation, reconciliation, and monitoring frameworks that guarantee the accuracy and quality of market datasets
• Maintain clear technical documentation covering pipeline logic, architectural decisions, and operational procedures

• 7-10 years' experience in software/data engineering, with a strong emphasis on Python and object-oriented development
• Demonstrable experience building or maintaining large-scale tick-data platforms or market-data ingestion systems
• Strong knowledge of data-lake architecture, columnar storage (Parquet essential), and modern table formats (Iceberg highly advantageous)
• Familiarity with cloud ecosystems (AWS, GCP, or Azure), including compute, storage, and workflow orchestration patterns
• Comfortable with Kubernetes, containers, and automated deployment workflows
• Strong communication skills and the ability to operate closely with quants, PMs, and trading-focused engineering teams
• A strong academic background in Computer Science, Engineering, Mathematics, or a related technical field
• (Preferred) Experience working with time-series databases such as KDB or OneTick; C++ exposure
• (Preferred) Prior exposure to financial markets - particularly equities, macro, or systematic strategies

[Up to c. £250k Comp Package | Hybrid Working]