Quantitative Data Engineer

At Harris, the true value of what makes us successful is found in our people. It is our unique mix of cultures, experiences, beliefs and backgrounds that sets Harris apart from the rest.  We constantly strive to cultivate, nurture and amplify an unparalleled environment, where we value intellectual curiosity and uniqueness of thought.  Inclusion is embedded in the very fabric of our culture of collaboration and openness.

We understand that a job description only tells one part of a broader story, and Harris is seeking dynamic candidates who can add to our best-in-class environment. We recognize that qualifications can be gained through both traditional and non-traditional paths, and we are committed to considering candidates who possess the potential to be excellent in this role regardless of prior experiences.  

Therefore we encourage ALL interested individuals to submit their applications, even if they do not meet every requirement outlined in the job description. 

The Position

The Quantitative Engineering team is responsible for designing, building, and maintaining the firm’s data infrastructure, cloud platforms, and integration frameworks that empower the Quantitative Research function. This includes delivering highly reliable, data-driven research capabilities, advanced analytics, and actionable portfolio insights to optimize portfolio construction, enhance risk reporting, and support ongoing research initiatives.

The Quantitative Data Engineer plays a pivotal role in developing and managing scalable data pipelines, analytical tools, and cloud-based infrastructure that enable the generation of actionable investment insights. Responsibilities include sourcing, curating, and integrating data from internal platforms and external market data providers into both research and production environments, while ensuring data accuracy, availability, and integrity. The role involves close collaboration with Quantitative Research leadership and cross-functional engineering and technology teams to enhance system resiliency, infrastructure scalability, and data architecture. Success in this role requires curiosity, a proactive mindset, and a strong dedication to maintaining data accuracy and integrity.

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

Qualifications

Required

• 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
• 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
• Effective project management skills with a track record of fast-paced, iterative delivery through proactive input gathering and cross-functional collaboration

Preferred

• End-to-end experience with the software development lifecycle (SDLC), using Agile/Scrum frameworks and tools such as Jira
• Proficient in version control systems (e.g., SVN, Git), including branching and merging workflows.
• Skilled in building and maintaining s