Founding Engineer - 21172

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  End-to-End System Ownership: Define system health metrics, instrument observability logging/alerting before shipping, and maintain what you build in cloud environments.

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  Design & Documentation Culture: Author design documents for consequential architectural decisions prior to writing code; maintain technical documentation as part of the strict definition of done.

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  Quality Assurance & AI Leverage: Aggressively utilize AI tools to accelerate build speed while taking 100% personal ownership of every line of code deployed.

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  Early Escalation & Transparency: Surface technical blockers, production anomalies, and architectural issues immediately to prevent cascading failures.

Appraisal Modeling: Develop, evaluate, and productionize statistical and machine learning models driving real-time asset valuation and solar asset appraisals.

Applied Analytics: Bridge statistical rigor with real-world financial and environmental outcomes, creating explainable valuation frameworks.

Professional Track Record: 3–5+ years of professional engineering experience shipping and operating production systems in cloud environments (AWS/GCP/Azure).

Core Technical Stack: Advanced proficiency in Python (production-quality code architectures) and/or track-relevant languages (TypeScript, SQL).

Database & SQL Mastery: Advanced SQL fundamentals, including schema design, complex query optimization, relational database modeling, and performance intuition.

US Work Authorization: Must be legally authorized to work in the United States without current or future visa sponsorship requirements.

Engineering Mindset: High agency, strong opinions weakly held, commitment to writing design docs before executing, and a customer-first focus.

Software Engineering Track: Experience with async programming, relational database optimization, containerized deployments (Docker/Kubernetes), and automated CI/CD pipelines.

Data Engineering Track: Experience with data modeling, orchestration tools (Airflow, Dagster, Prefect), data quality frameworks (Great Expectations, dbt), and event-driven data streaming.

Data Science / ML Track: Proven track record productionizing ML models, experimental design, and building applied analytics frameworks that drive financial decisions.

Infrastructure & Security: Infrastructure-as-Code (Terraform) and security fundamentals (IAM, secrets management).

Production Reliability & System Health: Zero unmonitored production outages or silent data pipeline failures.

Delivery Velocity & Preparation: Consistently moving from approved technical design documents to production-ready releases within sprint targets.

Data Integrity & Valuation Accuracy: Delivering reliable, audit-ready asset valuations that customers and partners can trust for financial decisions.