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 THE TEAM AND THE ROLE
We're hiring the founding Engineering Manager for Web Reporting, a new squad in India that will build Juniper Square's next-generation web reporting product.
Web reporting is one solution that serves GPs, LPs and fund administrators, delivered wherever each of them works. The team will build it AI-first and headless from day one: reporting logic and data exposed through APIs and agent-ready interfaces, so the same infrastructure can power our web UI, the investor portal, customer integrations and AI agents. The EM will help shape how AI changes both the product and how the team builds the Web reporting domain.
The squad sits in the Reporting domain alongside our existing PDF Reporting team in North America. You'll work closely with the Reporting product manager, design, and the data platform team, and you'll be the engineering lead for web reporting in India.
WHAT YOU'LL DO
Build the team. Hire, onboard and grow a squad of 5–7 engineers. Set the bar, the culture and the operating rhythm for a brand-new team.
Deliver the web reporting roadmap. Own execution from tech design through launch. Plan sprints, manage scope and dependencies, and ship predictably against roadmap milestones.
Lead technical direction. Build, review and approve tech designs for the new system. Juniper Square is a Python shop with React frontend and a Redshift-backed data warehouse. Push on data modeling, performance at scale, correctness of financial numbers, and security.
Make AI part of how the team works. Use AI coding and design tools to raise team throughput. Help evaluate where LLMs belong in the product, such as AI-assisted report templating, and where deterministic logic must stay.
Partner across time zones. Work with product, design, PDF Reporting and the data platform team in North America. Keep a few hours of daily overlap with Pacific time and make decisions and status visible in writing.
Own quality and operations. Set standards for testing, observability and incident response. Financial reports go to investors, so accuracy is non-negotiable.
Develop people. Run 1:1s, give direct feedback, write performance reviews and build career paths for every engineer on the team.
WHAT WE'RE LOOKING FOR
12 years in software engineering, including 3+ years directly managing an engineering team of 5 or more.
A track record of building or scaling a team: you've hired engineers, onboarded them and kept them.
Strong technical judgment across web applications and data. You can review designs for Python services, React/TypeScript frontends and SQL-heavy data pipelines, and spot the weak points.
Experience with data-intensive or financial systems: reporting, analytics, dashboards, or products where the numbers must be exact.
AI-forward in practice. You use AI tools in your own work every day and have brought them into a team's workflow. You can name specific results, not just experiments.
You've worked well with distributed teams, ideally with stakeholders in North America.
Clear, direct written and spoken English. You flag risks early and bring a recommendation along with the problem.
BONUS POINTS
Private markets domain knowledge: private equity, real estate, venture or credit funds, fund administration, or investor reporting.
Background in financial or investor reporting, such as capital account statements, capital call and distribution notices, or fund performance reporting.
Hands-on experience with Redshift or a similar cloud data warehouse, or with semantic layers and BI tools.
You've shipped a product feature that uses LLMs in production, including evaluation and guardrails.
You've been the first engineering manager on a new team or in a new location.
