Staff Software Engineer, Performance and Analytics
RidgelineReno, NVPosted 23 April 2026
Job Description
Are you a detail-oriented engineer with a passion for uncovering insights through quantitative analysis? Do you excel at solving complex financial challenges and driving data-driven decision-making? Are you looking for an opportunity to innovate and contribute to the development of a next-generation portfolio performance measurement system? Do you have expertise in performance measurement standards, such as GIPS, and the ability to leverage modern tools to deliver impactful results? If so, we invite you to join our forward-thinking team
We are seeking a Performance Measurement Attribution Engineer with deep expertise in portfolio performance measurement systems, attribution models, and investment data management to help us scale our GIPS-compliant performance and analytics platform. In this role, you’ll leverage your domain knowledge to design and enhance high-impact systems for performance measurement, tackle complex data challenges, and craft solutions that empower informed decision-making. Your ability to navigate industry standards, such as GIPS, will be critical in building systems that meet the highest benchmarks of accuracy and speed. By joining our dynamic FinTech startup, you’ll contribute to redefining investment management technology while fostering a collaborative and engaging work environment
You must be work authorized in the United States without the need for employer sponsorship.
The impact you will make:
Design portfolio performance measurement and attribution systems to support multiple reporting workflows
Propose technical design solutions and drive decision-making in a fast-paced, growing team
Partner closely with design, strategy, and engineering teams in an environment rooted in learning, teaching, and transparency
Engage in the entire development process, from requirements and design reviews to implementation and testing
Develop systems to handle complex asset classes, including derivatives, fixed income, and private equity
Optimize time-weighted returns (TWR), internal rate of return (IRR), and performance attribution calculations
Impact a developing tech stack based on AWS back-end services
Create automation that leads to scalable, high-quality applications that customers rely on to run their businesses
Coach, mentor, and inspire fellow engineers to deliver high-performing, secure enterprise applications
Think creatively, tackle complex problems, seek innovative solutions, and communicate clearly throughout the process
Contribute to a culture of collaboration, continuous learning, and transparency
What we look for:
5+ years of experience in software engineering with a strong background in architecting and designing performance reporting and analytics systems
Strong proficiency in performance measurement for equities, fixed income, derivatives, and alternative investments such as private equity and hedge funds
Completion of or progress towards CFA, CIPM or related performance measurement certification
Expertise in performance measurement concepts and methodologies, including TWR, Brinson Attribution, GIPS, and Profit and Loss analysis
Proficiency in working with Relational or Analytical databases, with the capability to optimize queries
Experience building cloud native applications on AWS/Azure/Google Cloud
A degree in Finance (relevant to performance measurement), Computer Science, Information Science, or a related discipline
Willingness to learn about new and develop expertise in varying technologies
Strong understanding of the value of automated testing at all levels
Ability to focus on short-term deliverables while maintaining a big-picture long term perspective
Strong problem-solving skills and an aptitude for tackling complex challenges
Excellent communication skills and the ability to engage with colleagues at all levels
A serious interest in having fun at work
Bonus:
Experience with GitHub Copilot or ChatGPT
Experience with Kotlin and/or other JVM Languages
Famili ... (truncated, view full listing at source)
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