Overview

We are seeking a talented and experienced Market Data Developer to take ownership of our complete market data infrastructure, covering both live real-time feeds and extensive historical data for Futures and Foreign Exchange (FX) products. This role is crucial for powering our systematic research and quantitative trading strategies. The successful candidate will be responsible for connecting to exchange feeds and third-party data vendors, building robust data processing pipelines, and creating the core infrastructure used by our quantitative researchers.

Key Responsibilities

• Data Acquisition & Connectivity: Design, develop, and maintain high-performance connectors and APIs to ingest real-time live market data from various sources, including direct exchange feeds (e.g., CME, ICE), broker connections, and third-party data vendor APIs (e.g., Bloomberg, Refinitiv, Reuters).
• Data Pipeline Development: Build, optimize, and manage scalable data pipelines (ETL/ELT) for processing, cleansing, and validating tick-by-tick and aggregated historical Futures and FX data.
• Systematic Research Infrastructure: Create and maintain the core infrastructure—including storage solutions (e.g., time-series databases, low-latency filesystems) and querying APIs—that enables quantitative researchers to efficiently access, backtest, and simulate strategies using both historical and live data.
• Data Modeling & Storage: Define and implement optimal data models for storing vast amounts of market data, prioritizing query performance, data integrity, and cost efficiency. Experience with time-series databases is a key requirement.
• Monitoring & Reliability: Implement comprehensive monitoring, alerting, and logging tools to ensure the continuous health, accuracy, and low-latency delivery of all market data feeds.
• Collaboration: Work closely with the quantitative research team to understand their specific data needs (e.g., specific fields, aggregation levels, data slicing) and translate them into robust technical solutions.

Required Qualifications

Technical Skills

• Experience: Minimum of 2+ years of professional experience in financial technology, specifically working with market data systems.
• Programming: Expert-level Python proficiency for data pipelines and research tooling, plus proficiency in a truly high-performance systems language (e.g., C++/Java) for low-latency components such as feed handlers.
• Data Handling: Proven experience working with very large datasets, specifically tick-by-tick market data. Deep familiarity with data structures and algorithms for time-series data processing.
• Database: Hands-on experience with time-series databases (e.g. InfluxDB, TimescaleDB) and/or high-performance columnar databases.
• APIs & Protocols: Direct experience integrating with and consuming data via commercial vendor APIs (e.g., Bloomberg, Refinitiv) and/or exchange APIs. Familiarity with common data protocols like FIX (specifically for market data) is a plus.
• Cloud/Infrastructure: Familiarity with cloud environments (AWS, GCP, or Azure) for data storage and processing, and experience with containerization (Docker/Kubernetes).

Domain Knowledge

• Asset Classes: Strong understanding of Futures and Foreign Exchange (FX) market data conventions, including concepts like bid/ask, trade prints, order book depth, and various market data license requirements.
• Quantitative Research: Basic understanding of systematic trading, backtesting methodologies, and the specific data requirements of quantitative models.

Overview

We are seeking a highly skilled Quantitative Developer to join our systematic trading team. This hybrid role is responsible for both building and maintaining our core Futures and Foreign Exchange (FX) market data infrastructure and utilizing that data to support and engage in quantitative research for systematic trading strategies. The successful candidate will combine expertise in high-performance data engineering with a strong understanding of quantitative finance and algorithmic research methodologies.

Key Responsibilities

• Market Data Infrastructure: Design, develop, and maintain high-performance, resilient systems for capturing, processing, and validating live (real-time) and historical tick-by-tick market data for Futures and FX.
• Quantitative Research Support: Serve as the technical bridge to the quant research team, ensuring their data requirements are met. This includes creating and optimizing the research environment (data access APIs, backtesting frameworks) and ensuring data consistency.
• Systematic Strategy Development: Actively participate in the research and development of systematic trading strategies by analyzing large market data sets, identifying alpha signals, and implementing models for testing and production deployment.
• Data Acquisition & Vendor Integration: Implement connectors and APIs to ingest data from various sources, including direct exchange feeds (e.g., CME, ICE) and third-party data vendor APIs (e.g., Bloomberg, Refinitiv).
• Data Modeling & Storage: Define, implement, and optimize data models in time-series databases to ensure ultra-fast querying and analysis by the research team.
• Code Optimization & Tooling: Improve the performance and efficiency of research and production code, and develop internal tools for data visualization, quality control, and pipeline monitoring.

Required Qualifications

Technical Skills

• Experience: Minimum of 3+ years of professional experience in quantitative finance, market data engineering, or systematic trading.
• Programming: Expert proficiency in Python (for quantitative modeling, backtesting, and data analysis) and strong proficiency in a compiled language like C++ or Java (for high-performance data processing/feed handlers).
• Data Handling & Storage: Mandatory, hands-on experience with time-series databases (e.g., InfluxDB, TimescaleDB) for storing and efficiently querying high-frequency market data.
• APIs & Protocols: Direct experience integrating with commercial vendor APIs (e.g., Bloomberg, Refinitiv) and/or exchange data feeds.
• Quantitative Libraries: Strong familiarity with Python's quantitative and data science ecosystem (e.g., Polars, Pandas, NumPy, Scikit-learn).

Domain Knowledge

• Asset Classes: Strong understanding of Futures and Foreign Exchange (FX) market data conventions, microstructure, and the unique challenges associated with their data (e.g., order book reconstruction, roll dates).
• Quantitative Finance: Solid understanding of systematic trading concepts, statistical analysis, time-series modeling, and the methodology behind quantitative strategy backtesting and simulation.

Preferred Qualifications

• Prior experience building or contributing to a proprietary algorithmic backtesting framework.
• Familiarity with distributed computing frameworks (e.g., Spark) for large-scale data processing.
• Advanced degree (Master's) in a quantitative field (e.g., Computer Science, Mathematics, Physics, Financial Engineering).