Senior Market Data Engineer

Responsibilities

• 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 back testing 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 back testing 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

• 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 and Go, or Rust.
• Familiarity with ETL (Extract, Transform, Load) processes (or other data pipeline architecture) and tools to clean, normalize, and validate large datasets.
• Experience working with distributed data systems and tools such as Hadoop, Kafka, Spark, or similar technologies.
• Linux/Unix expertise, particularly in managing and optimizing systems for data storage and processing.
• 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.
• Bachelor’s degree in Computer Science, Engineering, or a 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.
• Proficiency in containerization and orchestration —‍ Docker, Airflow, SLURM tools.
• 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.

What we offer

• Working in a modern international technology company without bureaucracy, legacy systems, or technical debt.
• Excellent opportunities for professional growth and self-realization.
• Remote work from anywhere in the world with a flexible schedule.
• Compensation for health insurance, sports activities, and non-professional training.