Our mission is to unlock the full potential of private markets. Privately owned assets like commercial real estate, private equity, and venture capital make up half of our financial ecosystem yet remain inaccessible to most people. We are digitizing these markets, and as a result, bringing efficiency, transparency, and access to one of the most productive corners of our financial ecosystem. If you care about making the world a better place by making markets work better through technology – all while contributing as a member of a values-driven organization – we want to hear from you.
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 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 The Role
We are building a strategic new backend platform service that will own a critical content lifecycle end-to-end — from initial creation through multi-stage internal review to external delivery — at multi-tenant scale, with audit-grade compliance.
This is a backend-heavy role — the core charter is distributed-systems engineering: event-driven architecture, Postgres at scale, durable eventing, multi-tenant performance, and production reliability. The service is also designed with first-class extensibility for AI use cases (foundation for RAG, AI-driven classification, and more). A working understanding of full-stack is a plus, but the primary bar is backend depth.
Why this role is exciting: You will be the single technical owner for this service from green-field through a multi-phase rollout — partnering closely with architecture, engineering management, and product leadership. You'll set the technical direction for a high-impact platform, lead a team of 4–5 engineers, and groom junior engineers into senior contributors. It's a high-autonomy, high-visibility role with a clear path to staff-level impact for the right candidate.
What You'll Do
Own the end-to-end backend architecture: event-driven core, Postgres partitioning, durable eventing patterns, multi-tenant scale
Design APIs, microservice boundaries, and service contracts for a high-throughput, multi-tenant platform
Build the AI integration surface: foundation for RAG, async classification, and other AI use cases
Drive technical phasing across multiple product phases (foundations → review workflows → external delivery → search/AI → migration)
Partner with platform architects to align with company-wide standards (eventing, observability, deploy)
Lead a team of 4–5 engineers technically — design reviews, code reviews, raising the engineering bar
Groom and mentor junior engineers — pair on hard problems, run learning sessions, give actionable feedback, and create a clear path for them to grow into senior engineers
Own production health — SLOs, runbooks, incident postmortems.
Must Have Skills
10+ years of backend engineering, 5+ years in Python (or strong polyglot — Python/Java)
Production experience with async Python (FastAPI / asyncio) at scale
Strong API design — REST/gRPC, versioning, schema evolution, backward compatibility
Microservices and distributed systems — service boundaries, contracts, eventing patterns, idempotency, outbox/saga
Deep PostgreSQL chops — partitioning, complex queries, replication, performance tuning
Scale & performance — capacity planning, caching, query optimization, latency/throughput trade-offs
AWS deep — ECS/EKS, S3, SNS/SQS, Aurora, IAM, IaC
Observability and production ownership — metrics, tracing, on-call, SLOs, postmortems
Experience leading project-level technical direction for 12+ months with measurable outcomes
Track record of grooming and mentoring junior and mid-level engineers — taking them from execution-level work to ownership of design and delivery.
Good to Have
Full-stack working knowledge — React, modern TypeScript, ability to reason about end-to-end request flow
Hands-on production AI integration with a major LLM provider (OpenAI / Anthropic / Bedrock)
Foundation for RAG / built a production RAG system with a vector DB (pgvector / OpenSearch / Pinecone)
MCP (Model Context Protocol) — built or seriously prototyped a custom MCP server, or worked with agent frameworks
Document management or content platform background; multi-tenant SaaS at scale
Compliance-driven system experience (SOC2 / SOX)
Open-source contributions in Python or AI tooling; fintech / asset management / SaaS domain exposure.