Original listing text, shown exactly as published by the company.
What you'll do
Own End-to-End Delivery of Core Data Platform Components
- 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
Drive Technical Architecture
- 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
Build AI Evaluation Infrastructure
- 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
Ship with AI-Native Development Practices
- 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
Establish Technical Standards
- 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
Qualifications
Required
- 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
Preferred
- 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
CompensationCompensation for this position includes a base salary, equity, and a variety of benefits. The U.S. base salary range for this role is $210,000 – $260,000 USD. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable.
