Analytics Engineer (Investment/Financial Services)

Frederick Fox; New York (NY); On-site; USD 130,000 - 190,000; Full time

Job summary

Frederick Fox is seeking an Investment Analyst, Quantitative Analytics and Technology to act as a strategic analytics partner for the Investment Office. You will translate analytical needs into practical, production-grade tools and dashboards that enhance investment decision-making across a multi-asset portfolio.

You will manage data assets in Azure, validate new data sources, ensure governance and security, and evaluate emerging AI and automation tools.

Qualifications

• Bachelor’s degree in a quantitative field or related discipline.
• 5+ years in data science, analytics engineering, software engineering, or related technical role.
• Experience building production-grade analytics solutions.

Responsibilities

• Identify opportunities where data, analytics, automation, and technology improve investment decision-making.
• Manage and expand the SQL database environment in Azure, integrate APIs, validate data sources, monitor data quality.
• Design, build, deploy, and maintain analytics tools, dashboards, data pipelines, and applications (e.g., Streamlit, LLM-based tools).
• Evaluate AI/ML and automation tools for secure, user-friendly workflows.
• Provide data-driven insights for portfolio management and investment evaluation.
• Support technology-related investment evaluations including venture opportunities.

Skills

Python

SQL

Data pipelines

Dashboards

Azure cloud

Streamlit

Excel

Education

Bachelor's degree

Graduate degree preferred

Tools

Azure

Tableau/Power BI

Streamlit

APIs

Job description

POSITION SUMMARY

The Investment Analyst, Quantitative Analytics and Technology will serve as a strategic analytics partner and internal consultant to the Investment Office, helping identify, design, build, and implement data-driven tools that support better investment decision-making across a complex, multi-asset class portfolio. This individual will work closely with investment professionals to understand workflows, translate analytical needs into practical solutions, and make high-quality investment analytics more accessible, intuitive, secure, and actionable.

The role will also help advance the Investment Office's existing data environment by managing existing data assets, adding and validating new data sources, and ensuring strong data integrity, governance, and security. This individual will assess emerging tools and technologies, manage relevant vendor relationships, and contribute to the evaluation of technology-related investment opportunities, including venture capital investments.

MAJOR RESPONSIBILITIES

• Partner with the Investment Office to identify opportunities where data, analytics, automation, and technology can improve investment decision-making, operational effectiveness, and reporting.


• Manage, maintain, and expand the Investment Office's SQL database environment in Azure, including integrating data from multiple APIs, adding and validating new data sources, documenting data flows, monitoring data quality, managing access, and optimizing database performance.


• Design, build, deploy, and maintain production-grade internal analytics tools, dashboards, data pipelines, and applications, including Streamlit and LLM based tools, that make investment data more accessible, customizable, secure, and actionable.


• Evaluate emerging AI, machine learning, and automation tools as the ecosystem evolves, with a focus on ease of use, security, analytical quality, workflow efficiency, and relevance to Investment Office needs. Explore secure AI-enabled workflows and data-access frameworks that help investment professionals interact more effectively with approved data, analytics, and internal knowledge sources.


• Participate actively in Investment Office meetings, contributing data-driven insights to support portfolio management, manager research, asset allocation, and investment evaluation.


• Support the evaluation of technology-related investment opportunities, including venture capital investments; collaborate with departments across to share best practices; and complete ad hoc-related projects with a data-forward, analytical, and solutions-oriented approach.

QUALIFICATIONS

The ideal candidate is a highly motivated, entrepreneurial, and technically sophisticated investment professional who combines analytical judgment with the ability to build practical, production-grade data and technology solutions.

COMPETENCIES

• Strong technical proficiency in Python and SQL, with experience building and maintaining databases, data pipelines, APIs, dashboards, and analytics applications.


• Experience working with cloud-based data environments, preferably Azure, including data ingestion, validation, documentation, access management, and performance optimization.


• Demonstrated ability to design, build, deploy, and maintain production-grade internal tools or applications for business users; experience with Streamlit, Python-based analytics applications, and Excel-based workflows strongly preferred; experience with Tableau, Power BI, or similar visualization platforms a plus.


• Familiarity with AI, machine learning, automation tools, LLM-enabled workflows, or emerging enterprise analytics technologies, including an understanding of data security, governance, and responsible use considerations.


• Demonstrated analytical, quantitative, and problem-solving capabilities, with excellent attention to detail and a commitment to data accuracy, integrity, security, documentation, and responsible use of data and technology.


• Collaborative and effective communicator with strong project-management skills, able to partner closely with a technically sophisticated investment team, translate analytical capabilities into practical investment applications, prioritize multiple projects, and operate effectively in a lean, high-impact team environment.

REQUIREMENTS

• Bachelor's degree required; graduate degree preferred in data science, computer science, engineering, applied mathematics, statistics, finance, business analytics, or a related quantitative field.


• Five or more years of relevant experience in data science, analytics engineering, software engineering, investment analytics, financial technology, quantitative research, or a related technical role.