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
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

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.

Senior Software Engineer, Data – AI

Key skills