Senior Software Engineer, Juniper Square

• This role is for someone who executes with high craft and speed, is fluent in agentic development as a first-class part of their workflow, and cares deeply about the reliability and usability of the systems they build
• 7+ years of software engineering experience, with a track record of owning and shipping production data systems end-to-end
• Hands-on experience building and operating data pipelines or data warehouse infrastructure: streaming or batch ingestion, ETL/ELT patterns, schema design, query optimization
• Experience with AWS data infrastructure; Redshift experience strongly preferred
• Comfort working across the data stack – from pipeline logic and storage to APIs and observability tooling
• Production experience building with LLMs – integrating models into real systems, not just experimentation
• Fluency with AI-assisted and agentic development workflows; you use these tools daily and evaluate their output critically
• Strong written communication – able to document technical decisions clearly for both engineering and non-technical audiences
• Background in financial services data – familiarity with fund administration, investment data schemas, or institutional reporting workflows is a meaningful differentiator
• Experience building self-service data platforms or developer-facing data tooling for internal consumers
• Familiarity with data lineage, data contracts, or metadata management patterns at scale
• Familiarity with data catalog or data governance platforms – open source (e.g. OpenMetadata) or commercial
• Experience building backend services using Python/FastAPI and designing and implementing scalable RESTful and GraphQL APIs
• Experience authoring modern web applications using ReactJS and TypeScript and designing reusable UI components and scalable frontend architecture

Desirable

• Juniper Square’s Data Platform team owns the foundational data infrastructure that powers reporting, analytics, and data access across the company
• The team built and operates the data fabric – a real-time streaming data layer built on Kafka, Flink, and Redshift – that serves as the company’s unified data access layer and underpins in-app reporting for customers
• The team is now expanding that foundation with a self-service data catalog, governance tooling, and a self-service ingestion and transformation platform built on dbt and Airflow
• As a Senior Software Engineer on the Data Platform team, you will own the delivery of meaningful components of this infrastructure – building and extending systems for data ingestion, transformation, cataloging, lineage, and observability
• Since these are self-service tools with significant admin surface, you will also be comfortable with full stack development as you build and maintain these surfaces
• You will write production code every day, contribute to technical architecture discussions, and help scale a platform that serves both internal teams and end customers
• 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
• Build and operate the data catalog platform, enabling stakeholders across the company to discover available data, understand business context, search by keyword, and trace lineage
• 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
• 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
• 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
• 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