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Staff Software Engineer, Investment Performance and AnalyticsSynthesia • United States
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Staff Software Engineer, Investment Performance and Analytics

Synthesia
  • US
    United States
  • US
    United States
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À propos

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
Familiarity with industry software and data sets such as Bloomberg, FactSet, Refinitiv, and ICE
About Ridgeline
Ridgeline is the first front-to-back system of record for investment managers. Founded by visionary entrepreneur Dave Duffield (co-founder of both PeopleSoft and Workday), the company was created to modernize an industry held back by outdated, disconnected technology. Powered by a single, real-time data set and embedded AI, Ridgeline helps firms automate complexity, accelerate collaboration, and deliver tailored client experiences at scale, without added headcount or risk. Ridgeline is headquartered in Lake Tahoe, with offices in New York, Reno, and the Bay Area, and is recognized by Fast Company as a "Best Workplace for Innovators," by Frost & Sullivan as a "Technology Innovation Leader," and by The Software Report as a "Top 100 Software Company."
Ridgeline is proud to be a community-minded, discrimination-free equal opportunity workplace.
Ridgeline processes the information you submit in connection with your application in accordance with the Ridgeline Applicant Privacy Statement. Please review the Ridgeline Applicant Privacy Statement in full to understand our privacy practices and contact us with any questions.
Compensation and Benefits
As an employee at Ridgeline, you'll have many opportunities for advancement in your career and can make a true impact on the product.
In addition to the base salary, Ridgeline employees can participate in our Company Stock Plan subject to the applicable Stock Option Agreement. We also offer rich benefits that reflect the kind of organization we want to be: one in which our employees feel valued and are inspired to bring their best selves to work. These include unlimited vacation, educational and wellness reimbursements, and $0 cost employee insurance plans. Please check out our Careers page for a more comprehensive overview of our perks and benefits.
  • United States

Compétences linguistiques

  • English
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