Senior Software Engineer, Data (AI)

Juniper Square is a company focused on unlocking the potential of private markets through technology and data solutions. They are seeking a Senior Software Engineer, Data to build a next-generation intelligent data platform, responsible for delivering core pipeline components and contributing to the technical architecture of an AI-native data warehouse.

Responsibilities:

• Build and ship production-quality implementations of data normalization, schema mapping, validation, enrichment, and distribution pipelines for a net-new intelligent data warehouse
• Write clean, well-tested, performant code across backend services, data pipeline logic, and API integrations
• Take end-to-end ownership of features from design through deployment, with accountability for correctness and reliability in production
• Work closely with Staff engineers to shape the architecture of a modern, AI-native data warehouse serving institutional financial clients
• Bring thoughtful input on schema design, normalization approaches, and API patterns - and execute those decisions with precision
• Identify and raise technical risks early; propose and implement solutions rather than waiting to be directed
• Use agentic coding tools and LLM-assisted development as your primary workflow - this is how the entire team operates
• Critically evaluate AI-generated code for correctness, edge cases, and regressions - shipping quality output regardless of how it was produced
• Contribute to the team's evolving practices around AI-accelerated development and testing
• Build and maintain data validation checks, monitoring, and observability tooling that keeps pipelines trustworthy at scale
• Participate in on-call and production support, diagnosing and resolving data quality issues quickly and thoroughly
• Write and maintain clear technical documentation for the systems you build

Requirements:

• 4–7 years of software engineering experience, with a track record of shipping production systems end-to-end
• Strong backend engineering fundamentals - backend services, data pipelines, and API design; we will ask you to walk through systems you personally built
• Hands-on experience with data pipeline or data warehouse engineering: ETL/ELT patterns, schema design, normalization, and data distribution
• Production experience building with LLMs - prompt design, model integration, and output validation in real systems
• Fluency with AI-assisted and agentic development workflows; you use these tools daily and evaluate their output critically
• Experience with AWS data infrastructure; Redshift experience a plus
• Strong written communication - able to document technical decisions clearly for engineering and product audiences
• Ability to critically evaluate AI-generated code and outputs, including identifying failure modes and regressions
• Experience with RAG pipelines, vector stores (e.g., OpenSearch, pgvector), or document extraction systems
• Background in financial services data - familiarity with fund administration, investment data schemas, or institutional reporting workflows is a meaningful differentiator
• Experience building data products for external customers, not just internal tooling
• Familiarity with evaluation frameworks for AI outputs: deterministic checks, cross-model comparison, or human-in-the-loop review patterns