The Quantitative Engineering team at Harris is responsible for building and maintaining the firm's data infrastructure, cloud platforms, and integration frameworks to support the Quantitative Research function. This role aims to deliver reliable, data-driven research capabilities, advanced analytics, and actionable portfolio insights to optimize portfolio construction, enhance risk reporting, and support ongoing research initiatives.
Requirements
- Proficient in Python for data science, statistical analysis and ETL
- Proven ability to design data pipelines on Microsoft Azure with continuous delivery, automation, and orchestration
- Skilled in visualization tools and APIs
- Advanced SQL proficiency with in-depth knowledge of relational databases, data warehousing, and modern data architecture
- Hands-on experience managing proprietary platforms supporting quantitative research, order management, trading, and research operations
- Proficient in version control systems (e.g., SVN, Git), including branching and merging workflows.
- Skilled in building and maintaining scalable data pipelines using Databricks
Responsibilities
- Build pipelines and data infrastructure that support the Quantitative Research team in conducting historical analysis, back testing, and optimization, and assist in developing tools to aid portfolio managers with portfolio construction and management
- Contribute to the design, implementation, and maintenance of quantitative models, proprietary analytic tools that support portfolio construction and research
- Assist in the development and execution of risk attribution and reporting processes for investment teams and clients
- Support the Head of Quantitative Research, portfolio managers, analysts, and investment team members with custom research and client-oriented projects
- Maintain development environments, operational workflows, and support systems for the firm’s quantitative technology and data platforms
- Develop expertise in vendor platforms (e.g., FactSet, Bloomberg) to enhance Quantitative Research workflows and data management, augmenting internal tools
- Implement data governance, lineage, and monitoring solutions to ensure data integrity, traceability, and operational reliability across quantitative systems
Other
- Bachelor’s degree in engineering, Finance, Statistics, Computer Science, or Data Science (STEM discipline preferred); advanced degree is a plus
- 1–3 years of experience, preferably in financial services or investment management
- Understanding of portfolio theory, risk management, and investment analytics
- Effective project management skills with a track record of fast-paced, iterative delivery through proactive input gathering and cross-functional collaboration
- Familiarity with optimizers and factor risk models to support quantitative analysis and portfolio construction
