openashbyhqfelicis
Staff Software Engineer, Data (AI)
Juniper Square
LocationAmericas (USA or Canada)
WorkplaceRemote
EmploymentFullTime
Posted2026-05-26T16:17:05.580+00:00
Last observed2026-07-02 05:05:44.220281
Job idfelicis-juniper-square:ashbyhq:c499f5d0-bc2f-47a6-8058-415d3cb4d2a3
ABOUT JUNIPER SQUARE Private markets are one of the largest, most complex, and most underserved corners of global finance. Our mission at Juniper Square is to unlock their full potential. We’re the Operations Partner trusted by 2,300+ GPs, unifying technology, data, and fund administration services into a single platform that helps GPs move faster, make better decisions, and scale with precision. With $300B+ under administration and 700,000+ LPs on platform, we’ve built the scale to match our ambition. And with JunieAI, our purpose-built AI platform, we’re reimagining how private markets operate, embedding intelligence across every workflow. Founder-led since 2014, backed by $350M+ in funding, and now 1,000+ employees strong, we’re building a company designed to shape the future of private markets for decades to come. Our culture is built for people who want to do ambitious, meaningful work alongside exceptionally talented teammates. We think like owners, move with urgency, and take pride in solving hard problems that truly matter to our customers and the future of private markets. We believe the best ideas come from open debate, deep collaboration, and diverse perspectives, which is why we believe transparency is the default and feedback makes us stronger. If you’re energized by high standards, rapid growth, and the opportunity to help define a category at a pivotal moment, come join us! Juniper Square offers employees a variety of ways to work, ranging from a fully remote experience to working full-time in one of our physical offices. We invest heavily in digital-first https://blog.junipersquare.com/juniper-square-ponders-future-of-office-with-digital-first-hybrid-workplace-strategy/ operations, allowing our teams to collaborate effectively across 27 U.S. states, 2 Canadian Provinces, India, Luxembourg, and England. We also have physical offices in San Francisco, New York City, Mumbai and Bangalore for employees who prefer to work in an office some or all of the time. ABOUT YOUR ROLE We are building a next-generation intelligent data platform for private markets - a greenfield initiative that will reshape how financial data is ingested, normalized, validated, enriched, and distributed across a complex ecosystem. This is a foundational role on a small, high-caliber seed team working at the intersection of modern data engineering and applied AI. As a Staff Software Engineer, Data, you will own the design and hands-on delivery of the core pipeline components that make this platform work: schema mapping, data normalization, validation, enrichment, and distribution to downstream systems. You will write production code every day, make consequential architectural decisions, and help establish the technical standards and practices the broader team will build on as it scales. This role is for someone who thrives at the frontier of how software gets built - using AI-assisted and agentic development as a first-class part of their workflow - and who wants the challenge and ownership that comes with building something genuinely new. WHAT YOU'LL DO Own End-to-End Delivery of Core Data Platform Components • Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse • Write production code as a hands-on individual contributor - this is not a role that delegates implementation to others • Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness Drive Technical Architecture • Partner with a small seed team to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients • Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches • Evaluate and select technologies with a bias toward what ships well and scales sustainably Build AI Evaluation Infrastructure • Design and imple
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