Historical Data Capture and Storage: Design develop and maintain systems for the acquisition storage and retrieval of historical market data from multiple financial exchanges brokers and market data vendors
Data Integrity and Accuracy: Ensure the integrity and accuracy of historical market data including implementing data validation cleansing and normalization processes.
Data Architecture Development: Build and optimize data storage solutions ensuring they are scalable high-performance and capable of managing large volumes of time-series data.
Versioning and Reconciliation: Develop systems for data versioning and reconciliation to ensure that changes in exchange formats or corrections to past data are properly handled.
Data Source Integration: Implement robust integrations with various market data providers exchanges and proprietary data sources to continuously collect and store historical data.
Data Access Tools: Build internal tools to provide easy access to historical data for research and analysis ensuring performance ease of use and data integrity
Collaborate with Trading and Research Teams: Work closely with quantitative researchers and traders to understand their data requirements and optimize the systems for data retrieval and analysis for backtesting and strategy development.
Performance and Scalability: Develop scalable solutions to handle growing volumes of historical market data including ensuring efficient queries and data retrieval for research and backtesting needs.
Optimize Storage Costs: Work on optimizing data storage solutions balancing cost-efficiency with performance and ensuring that large datasets are managed effectively.
Compliance and Auditing: Ensure historical market data systems comply with regulatory requirements and assist in data retention integrity and reporting audits.
Qualifications :
Required Skills and Experience
Commercial experience of financial instruments and markets (equities futures options forex etc.) particularly understanding how historical data is used for algorithmic trading.
Familiarity with market data formats (e.g. MDP ITCH FIX SWIFT proprietary exchange APIs) and market data providers.
Strong programming skills in Python (Go/Rust is a nice to have)
Familiarity with ETL (Extract Transform Load) processes (or other data pipeline architecture) and tools to clean normalize and validate large datasets.
Commercial experience in building and maintaining large-scale time series or historical market data in the financial services industry.
Strong SQL proficiency: aggregations joins subqueries window functions (first last candle histogram) indexes query planning and optimization.
Strong problem-solving skills and attention to detail particularly in ensuring data quality and reliability.
Bachelors degree in Computer Science Engineering or related field.
Preferred Qualifications
Experience in a proprietary trading firm or buy-side environment working with historical market data and its vendors.
Experience with data governance and compliance related to financial data storage and retrieval.
Experience in working with distributed data systems and tools such as Hadoop Kafka Spark or similar technologies.
Proficiency in containerization orchestration - Docker Airflow SLURM tools.
Linux/Unix expertise particularly in managing and optimizing systems for data storage and processing.
Experience with cloud-based storage solutions such as AWS S3 Google Cloud Storage or Azure and the ability to optimize for performance and cost.
Familiarity with machine learning and data science workflows to support quantitative research teams.
Additional Information :
What we offer:
Remote Work :
Yes
Employment Type :
Full-time
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