Senior Data Architect (USA)

Responsibilities

• Arcitect and implement a unified data platform that integrates hundreds of vendor datasets, providing consistent, accessible, and high-quality data to simulators and researchers
• Design efficient storage and retrieval systems to support both large-scale historical backtesting and high-frequency research workflows
• Develop intuitive researcher interfaces and APIs that allow users to easily discover variables, explore metadata, and assemble data into standardized stocks × values matrices for rapid hypothesis testing
• Collaborate closely with quantitative researchers and simulation teams to understand their workflows, ensuring the data platform meets real-world analytical and performance needs
• Establish best practices for data modeling, normalization, versioning, and quality control across asset classes and data vendors
• Work with infrastructure and DevOps teams to optimize data pipelines, caching, and distributed storage for scalability and reliability
• Prototype and deploy internal data applications that enhance research productivity and data transparency
• Mentor and guide data engineers to maintain robust, maintainable, and well-documented data systems

Requirements

• 7+ years of experience in data architecture, quantitative research infrastructure, or large-scale data engineering in a financial or research-driven environment
• Proven experience designing and implementing scalable data storage solutions (e.g., columnar databases, time-series systems, object stores, or data lakes)
• Strong proficiency in Python and familiarity with modern data stack technologies (e.g., Parquet, Arrow, Spark, SQL/NoSQL, distributed file systems)
• Deep understanding of time-series and financial data modeling, including handling multiple vendors, instruments, and frequencies
• Experience building data interfaces, APIs, or tools that serve researchers, data scientists, or quantitative analysts
• Ability to translate research needs into efficient data schemas and access patterns
• Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, Mathematics, or a related quantitative field
• Strong collaboration, communication, and documentation skills
• Familiarity with cloud-based architectures (e.g., AWS, GCP, Azure) and modern data governance practices is a plus