Quantitative Data Engineer

Harris Associates•Chicago, IL

107d•$100,000 - $125,000

About 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.

Requirements

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

Nice To Haves

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 scalable data pipelines using Databricks
Familiarity with optimizers and factor risk models to support quantitative analysis and portfolio construction
Experience with financial data platforms, including FactSet, Bloomberg, and Morningstar
Understanding of cloud-native principles and Software as a Service (SaaS) architecture

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