Data Architecture & Engineering. Design, build, and maintain scalable data pipelines and architecture across Snowflake, dbt, and AWS. Lead technical decisions around ingestion, transformation, orchestration, and data modeling, ensuring our infrastructure is reliable, performant, and built to scale.
Data Integrations & Foundational Modeling. Develop and improve integrations with internal systems, merchants, payment platforms, and external partners. Build well-structured, reusable dbt models that transform raw source data into trusted datasets for downstream analytics, financial systems, and reporting.
Data Quality & Governance. Establish standards for data testing, monitoring, validation, documentation, and governance. Implement automated controls to identify issues early, improve data integrity, and ensure data is traceable and reliable across systems.
Cross-Functional Partnership. Partner with Engineering, Analytics, Finance, Operations, and Financial Systems to understand business requirements, define data specifications, and ensure downstream teams have the reliable data they need. Communicate technical concepts and tradeoffs effectively to both technical and non-technical stakeholders.
Technical Leadership & Continuous Improvement. Drive engineering best practices across SQL, dbt, code reviews, CI/CD, and development workflows. Identify opportunities to optimize performance, simplify architecture, and reduce manual processes. Mentor teammates, provide technical guidance, and help shape the long-term direction of our analytics engineering platform.
AI-Enabled Development. Incorporate AI coding agents and tools into everyday development workflows. Identify repeatable processes that can be turned into reusable skills, automated checks, and agent-driven workflows, while maintaining strong engineering standards and rigorously reviewing AI-generated code.