Senior Staff Software Engineer, Data

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 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 seeking a Senior Staff Software Engineer to lead the transformation of our current data engineering and analytics function into a modern, scalable, product-oriented Data Platform organization. You will define the vision, architecture, operating model, and execution roadmap required to evolve from project-based data delivery to a platform that enables self-service, reliable, governed, and analytics-ready data across the company.

This is a deeply hands-on leadership role for a technical expert who actively designs systems, prototypes solutions, reviews code, and guides teams through complex challenges. You will modernize our data stack, establish platform standards, introduce best practices for reliability and governance, and enable teams across the business to build data products efficiently and safely.

In addition to platform transformation, you will ensure the data ecosystem delivers high-quality analytics and actionable insights. You will define architecture across ingestion, processing, modeling, semantic layers, analytics, and AI/ML enablement, ensuring data is trustworthy, accessible, secure, and performant.

You will work closely with engineering leadership, product teams, analytics, and executive stakeholders to align technology strategy with business outcomes, mentor engineers, and build a data-driven culture. Success in this role means not only delivering a modern platform but also elevating the team’s capabilities, processes, and ways of working to operate as a true Data Platform organization.

What you’ll do

Architecture & Technical Leadership

Define and own the end-to-end data and analytics architecture strategy

Design scalable batch, streaming, and real-time data systems

Establish standards for data modeling, semantic layers, and reporting

Lead architecture reviews and technical decision-making

Drive adoption of modern architectures (lakehouse, data mesh, real-time analytics)

Hands-On Engineering

Design and prototype critical data platform components

Write production-quality code for complex or high-impact areas

Review schemas, transformations, dashboards, and analytics models

Troubleshoot performance and reliability issues across pipelines and queries

Optimize workloads for latency, concurrency, and cost

Data Platform & Pipeline Ownership

Design and architect a scalable data platform supporting ingestion, transformation, and delivery of both structured and unstructured data across batch and real-time pipelines.

Design a "Data for Agents" strategy, ensuring our data warehouse is structured with the semantic layers and metadata necessary for LLMs to navigate it accurately.

Build AI-ready data infrastructure, including vector stores, embedding pipelines, and retrieval systems that power LLM and agentic workflows.

Develop a RAG-ready data architecture that enables trusted enterprise data retrieval with strong lineage, governance, security, and observability.

Create curated data products and reusable APIs that make high-quality datasets easily consumable by applications, analytics platforms, and AI agents.

Enable self-service data access for engineering, analytics, and business teams through standardized models, semantic layers, and platform capabilities.

Partner with AI, product, and engineering teams to support training datasets, feature stores, and production AI inference pipelines.

Build agentic ETL/ELT pipelines that use AI agents to autonomously discover sources and generate transformations.

Ensure reliability, scalability, and resilience of the platform, including high availability, monitoring, and disaster recovery readiness.

Analytics & Business Intelligence

Partner with product, finance, business operations, and leadership teams to define analytics needs

Design scalable data models for reporting and advanced analytics

Ensure analytics solutions are performant, trustworthy, and easy to use

Drive adoption of data-driven culture through reliable insights

Governance, Quality & Security

Define data governance, lineage, cataloging, and metadata standards

Establish data quality frameworks and validation processes

Ensure privacy, compliance, and secure access to sensitive data

Implement role-based access controls and auditability

Leadership & Collaboration

Mentor senior engineers, analytics engineers, and data scientists

Partner with product, ML, platform, and business teams

Translate business questions into scalable data solutions

Influence roadmaps using data platform and analytics considerations

Act as the executive technical authority for data and analytics

Operational Excellence

Define SLAs/SLOs for data availability, freshness, and accuracy

Establish monitoring, alerting, and incident response processes

Optimize cloud costs and query performance

Support capacity planning for data growth

Culture & Enablement

Be an evangelist for pragmatic AI adoption.

Help establish a culture of outcome-driven innovation.

Qualifications

Required

Advanced degree in Computer Science, Engineering, or related field

15+ years in data engineering, analytics engineering, or data platform roles

Proven experience architecting large-scale data and analytics systems

Strong hands-on experience with modern data stacks in cloud environments

Deep expertise in data modeling for analytics (dimensional, star/snowflake, Data Vault, etc.)

Advanced SQL skills and proficiency in Python, Scala, or Java

Advanced expertise in dimensional data modeling and semantic layers (e.g., dbt, Cube) to provide "agent-readable" context.

Expertise in real-time streaming frameworks such as Spark, Flink, or Beam.

Experience building reporting and BI solutions at scale

Strong understanding of both batch and real-time architectures

Hands-on experience with AWS, Azure, or GCP data services

Experience with BI tools (e.g., Looker, Tableau, Power BI, etc.)

Strong understanding of data governance and security best practices

Ability to operate at both e