Staff Software Engineer, Data (AI)

• 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

• 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

• 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

• 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

• 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

• 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

• 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