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