Staff Software Engineer, Data (AI)

Juniper Square•,

21h•$210,000 - $260,000•Hybrid

About The Position

Juniper Square is building a next-generation intelligent data platform for private markets. This is 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.

Requirements

7+ years of software engineering experience, with demonstrated Staff-level technical scope and impact.
A portfolio of shipped production systems - we will ask you to walk through specific technical decisions you personally made and code you personally wrote; this is not a role for someone whose primary contribution has been directing others.
Strong hands-on experience with data pipeline or data warehouse engineering: schema design, ETL/ELT patterns, normalization, and API-based data distribution.
Production experience building with LLMs: prompt design, model orchestration, evaluation, and output validation in real systems, not just experimentation.
Fluency with AI-assisted and agentic development workflows; you use these tools daily and have strong opinions about how to use them effectively.
Experience with AWS data infrastructure; Redshift experience a plus.
Strong written communication — able to translate technical design into clear documentation for both engineering and product audiences.
Ability to critically evaluate AI-generated code and outputs, including identifying failure modes, regressions, and edge cases.

Nice To Haves

Experience with RAG pipelines, vector stores, or document extraction systems.
Background in financial services data — familiarity with fund administration, investment data schemas, institutional reporting workflows, or related domains is a meaningful differentiator.
Experience building data products or managed data services for external customers, not just internal tooling.
Prior experience in a technical lead or TLM capacity on a new or early-stage product team.

Responsibilities

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 implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts.
Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows.
Contribute to shared AI evaluation infrastructure that can serve as a foundation across multiple products.
Ship with AI-Native Development Practices: Use agentic coding tools and LLM-assisted development as your primary workflow - this is how the entire team operates.
Bring strong opinions about how to get the most from AI-assisted development while maintaining quality and reliability.
Contribute to the team's evolving practices around AI-accelerated SDLC.
Establish Technical Standards: Set coding standards, review practices, and architectural documentation that will scale as the team grows.
Help define what "good" looks like for a team building at speed without sacrificing quality.
Mentor engineers and provide technical guidance as the team expands.