• Own critical components end-to-end and deliver measurable improvements in system quality, scalability, and reliability. • Own delivery for significant features or systems: clarify requirements, design solutions, execute, ship, and iterate. • Design and build scalable backend services, APIs, and data pipelines that support core product functionality. • Drive system design decisions, balancing scalability, performance, reliability, and cost. • Define how quality is measured for your area (success metrics, testing strategy, observability) and automate it wherever possible. • Lead structured experimentation: define hypotheses, evaluate results, and translate insights into product and system improvements. • Improve operational readiness: deployment automation, monitoring/alerting, incident response, and performance optimization. • Mentor engineers through design reviews, code reviews, and hands-on technical guidance. • Influence engineering best practices, architecture decisions, and long-term technical direction.

• As a member of the Data and Storage Services organization, you will collaborate with other teams — including Product, Infrastructure, Lakehouse Infra, Lakehouse Analytics and Analytics Engineering to: • Architect and evolve Affirm's lakehouse analytics platform, driving strategy around Snowflake, Apache Iceberg, and Spark to deliver scalable, high-performance analytical infrastructure. • Design and implement robust Role-Based Access Control (RBAC) and dynamic data masking policies in Snowflake, ensuring data access is secure, compliant, and auditable across the organization. • Lead the technical direction of analytics engineering practices, including data modeling, transformation pipelines (dbt), and data quality frameworks that enable trustworthy, self-service analytics. • Drive data governance and privacy engineering initiatives, leveraging tools like Atlan to manage data cataloging, lineage, classification, and policy enforcement. • Identify and execute cost optimization strategies across Affirm's analytical compute and storage footprint, including Snowflake warehouse tuning, query optimization, and efficient data lifecycle management. • Collaborate with product engineering, data science, and business intelligence teams to understand their data needs and provide continuous guidance on design, architecture, and best practices. • Establish and champion best practices for lakehouse operations at scale, including schema evolution, table maintenance, partitioning strategies, and observability. • Stay ahead of industry trends in analytical data platforms, data governance, and privacy technologies, and identify opportunities to innovate and improve our data offerings. • Mentor engineers across the Lake Analytics Platform and Analytics Engineering teams, providing guidance on emerging technologies, development practices, and fostering a culture of technical excellence. • Participate in an on-call rotation and collaborate with other teams such as SRE to resolve production issues.

• Collaborate with other teams — including Product, Infrastructure, Lakehouse Infra, Lakehouse Analytics and Analytics Engineering to architect and evolve Affirm's lakehouse analytics platform, driving strategy around Snowflake, Apache Iceberg, and Spark to deliver scalable, high-performance analytical infrastructure. • Design and implement robust Role-Based Access Control (RBAC) and dynamic data masking policies in Snowflake, ensuring data access is secure, compliant, and auditable across the organization. • Lead the technical direction of analytics engineering practices, including data modeling, transformation pipelines (dbt), and data quality frameworks that enable trustworthy, self-service analytics. • Drive data governance and privacy engineering initiatives, leveraging tools like Atlan to manage data cataloging, lineage, classification, and policy enforcement. • Identify and execute cost optimization strategies across Affirm's analytical compute and storage footprint, including Snowflake warehouse tuning, query optimization, and efficient data lifecycle management. • Collaborate with product engineering, data science, and business intelligence teams to understand their data needs and provide continuous guidance on design, architecture, and best practices. • Establish and champion best practices for lakehouse operations at scale, including schema evolution, table maintenance, partitioning strategies, and observability. • Stay ahead of industry trends in analytical data platforms, data governance, and privacy technologies, and identify opportunities to innovate and improve our data offerings. • Mentor engineers across the Lake Analytics Platform and Analytics Engineering teams, providing guidance on emerging technologies, development practices, and fostering a culture of technical excellence. • Participate in an on-call rotation and collaborate with other teams such as SRE to resolve production issues.

• Writing secure, correct, efficient, clear and supportable “production ready” code that meets internal standards and industry best practice. • Enhance and extend our existing products, including device management, microservices, and WPF (Windows Presentation Foundation) applications to deliver seamless experiences to our customers. • Design and build APIs that integrate with internal and external services and contribute to overall system functionality. • Clarifying and communicating engineering requirements for new products, features, quality improvements, or design changes. • Track progress, update stakeholders, communicate with peers and broader team through stand-ups, regular updates on work items. • Implement industry-standard security practices to safeguard both customer and internal data. • Ensure thorough testing of code, participate in code reviews, and proactively resolve bugs and performance bottlenecks.