• Lead a high performing backend engineering team (Kotlin & Python) • Collaborate with Product, Analytics, Design, Operations, and other engineering teams to develop a technical roadmap that meets customer & business needs • Coach, develop, retain, and provide thoughtful feedback to team members • Hold a high technical bar with a bias towards longevity and simplicity • Foster a team culture around innovation, accountability, empathy, and execution • Collaborate & align with other engineering leaders across Affirm

• Lead and support a small solution engineering team focused on reusable libraries, shared capabilities, and platform enablement across product teams • Provide coaching, feedback, and support to a small, senior ‑ heavy engineering team, contributing to hiring and team development as needed • Collaborate with product, architecture, QA, and engineering teams to enable delivery through shared standards, guidance, and coordinated adoption of platform solutions • Drive adoption of modern engineering practices across CI/CD, deployment, scanning, and scaling, taking a technology‑agnostic approach aligned to platform needs • Serve as an escalation entry point (Level 4) for complex application‑related production issues, triaging incidents and coordinating resolution across teams while supporting initiative planning and prioritization • Champion secure application development practices and compliance expectations, supporting vulnerability remediation and secure SDLC adoption across teams • Develop clear change management approaches, technical guidance, and adoption plans to reduce cross‑team friction and cognitive load • Continuously assess engineering practices and tooling to promote pragmatic improvements in developer productivity and operational efficiency

• Design, build, and manage reliable data ingestion and transformation pipelines using Airflow, dbt, Python, Matillion, and Snowflake. • Develop and Manage automation frameworks for key Finance metrics like ACV/ARR and support Month End/ Quarter End close. • Collaborate closely with the US Finance Data Engineering team to define, standardize, and deliver business metrics, KPIs, and curated data models. • Balance the development of new capabilities with the need to scale and optimize existing systems. • Ensure reliability, scalability, and security of Snowflake data solutions and integrations. • Oversee multiple projects simultaneously, managing priorities, resources, and delivery timelines in a dynamic, high-velocity environment. • Communicate effectively with business and technical stakeholders to align data engineering initiatives with strategic organizational goals.

• Quality architecture. Frameworks, toolchain, and the roadmap from traditional automation to agentic solutions. You define how quality is built into the system, not bolted on at the end. • A team of 6-10 AQEs. Mentoring, technical direction, and running hands-on pilots alongside them as the team evolves into the agentic era. • The existing test automation framework (Java/JS · Selenium · Playwright). Keeping it healthy while evolving it; refactoring what needs it, retiring what doesn't. • Agentic QE design. Partnering with engineering leaders to co-design deterministic agent workflows and non-deterministic (LLM-assisted) approaches, with clear human-in-the-loop governance where it matters. • Observability and reporting tooling. Coverage dashboards, flakiness detection, CI quality gates - the visibility layer engineers actually trust. • Testing AI-powered features. Behavioral validation, non-deterministic regression, and output evaluation for the product's own agentic capabilities. • Internal tooling. Building tools that make the broader engineering org faster and more quality-conscious, not just the QE team.