Juniper Square - Senior Software Engineer, Data (AI)
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 • Preferred • 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
Responsibilities
• Build and Extend Core Data Infrastructure • Design and ship production-quality enhancements to the data fabric platform, including streaming ingestion pipelines (Kafka, Flink), Redshift-based storage and query layers, and data distribution to consuming teams and products • Contribute to the self-service ingestion and transformation platform – enabling domain teams to publish data models and analysts to create aggregations without blocking on data engineering • Extend and maintain the CI/CD pipeline for dbt transformations, including validation frameworks that prevent regressions as the model library grows • Own Data Catalog and Governance Capabilities • Implement integrations between the catalog and upstream systems – dbt, query history, data publishers – to keep lineage and metadata accurate and current • Contribute to governance tooling that ensures data quality, compliance, and observability across the platform as usage scales • Build and Operate with AI-Native Practices • 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 before shipping • Bring and share strong opinions on how to use AI tooling effectively across the full software development lifecycle • Drive Quality, Reliability, and Observability • Build and maintain monitoring, alerting, and observability tooling that keeps data pipelines healthy and issues detectable before they affect consumers • Write well-tested, performant code and participate actively in code reviews, raising the technical bar for the team • Produce clear technical documentation for the systems you build, enabling self-service adoption by internal teams • Collaborate Across Engineering and Product • Partner with your engineering manager, peer engineers, and data consumers (domain teams, analysts, product engineers) to translate requirements into well-scoped technical work • Contribute to cross-functional alignment on data contracts, schema standards, and ingestion patterns – helping prevent duplicated logic and siloed implementations across teams • Develop deep domain knowledge of private markets data – fund administration, investment workflows, reporting requirements – to build infrastructure that serves real business needs
Benefits
• Compensation for this position includes a base salary, equity, and a variety of benefits. The U.S. base salary range for this role is $185,000 – $225,000 USD. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable. • Health, dental, and vision care for you and your family • Life insurance • Mental wellness coverage • Fertility and growing family support • Flex Time Off in addition to company-paid holidays • Paid family leave, medical leave, and bereavement leave policies • Retirement saving plans • Allowance to customize your work and technology setup at home • Annual professional development stipend • Your recruiter can provide additional details about compensation and benefits.
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