• Designing and creating tables within data marts, data lakes and data warehouses • Building systems that collect, manage, and convert raw data into usable information for analytics • Expanding and optimizing data and data pipeline architecture • Mentoring junior data engineers and promoting data education

• Consult with teams across the organization to translate key product and business processes into technical data requirements. Use dbt, BigQuery, and SQL to build up Packback’s metrics layer to enable self-serve access to core analytics. • Build, optimize, and orchestrate data pipelines with dbt, Airflow, and Python • Establish data integrity standards and SLAs to ensure timely, accurate delivery of data • Build insightful and reliable dashboards to track performance of core company metrics across the product and business • Collaborate with business operations teams to define and train on best practices with respect to data modeling, analytics, and visualization • Collaborate with leadership to conduct analyses and apply statistical methods to answer day-to-day questions or support larger initiatives • Work within Packback’s research function with partner institutions and perform in-depth analyses to understand how Packback’s products are driving key student outcomes • Consult on the adoption of new technologies that impact Packback’s data infrastructure

• Own the analytics modeling lifecycle for a business domain (GTM / Lead-to-Customer or Post-Sales) — build and maintain dbt models and business marts on top of our source data in Snowflake. • Develop and maintain the semantic layer that powers our BI platform. • Partner directly with stakeholders across Finance, Sales, Marketing, CS, and RevOps to translate ambiguous business questions into the right model — prescribing or designing one when none exists. • Define, document, and enforce consistent metric definitions and segmentation across the org (e.g., customer lifecycle stages, production lifecycle, ARR), establishing whether each lives in dbt or the semantic layer so metrics aren't defined twice. • Build trustworthy self-serve data products so business partners can answer routine questions without help. • Collaborate with the Data Platform team where raw data is handed off — specify and request new sources, validate data, and raise quality issues upstream.

• Build and operate core platform capabilities: Design and implement platform features that enable engineers across Affirm to build, deploy, and operate data applications at scale — covering automated provisioning, deploy pipelines, access control, service lifecycle management, and reliability tooling. • Develop integrations: Build and maintain integrations between the platform and internal systems — CI/CD pipelines, identity and secrets management, external APIs, and event-driven hooks — so that application teams have secure, reliable, and self-service access to the tools they need. • Strengthen data access and governance: Design and operate secure data access patterns across the platform's analytical infrastructure, including RBAC, managed-access schemas, dynamic data masking, secure views, and cross-database grants that make platform data trustworthy without creating operational bottlenecks. • Automate toil and unlock self-service: Identify recurring support patterns — access provisioning, data pipeline failures, service health management, secrets management — and build the automation and tooling to make them self-service for platform users, eliminating manual admin work. • Improve observability and incident response: Build telemetry, alerting, and log infrastructure so platform teams and application owners can diagnose and resolve issues independently. Establish runbooks and operational patterns that reduce blast radius and recovery time. • Support platform operations: Participate in on-call coverage, triage production incidents across platform services and the applications that run on them, and execute platform-level maintenance with care for the engineers and workflows that depend on the platform. • Contribute to the emerging platform roadmap: Help design and build next-generation platform capabilities — semantic layer infrastructure, agentic application patterns, and AI-native data tooling — as the platform grows to support Affirm's most ambitious data use cases. • Raise the engineering bar: Set and hold high standards in code review, design, operational readiness, and incident follow-through. Help establish patterns that other contributors can follow and build on.