• 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.

• 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 • Develop integrations: Build and maintain integrations between the platform and internal systems • Strengthen data access and governance: Design and operate secure data access patterns across the platform's analytical infrastructure • Automate toil and unlock self-service: Identify recurring support patterns and build the automation and tooling to make them self-service • Improve observability and incident response: Build telemetry, alerting, and log infrastructure for diagnosing and resolving issues • Support platform operations: Participate in on-call coverage, triage production incidents across platform services • Contribute to the emerging platform roadmap: Help design and build next-generation platform capabilities • Raise the engineering bar: Set and hold high standards in code review, design, operational readiness

• Work across our multidisciplinary consultancy team, with a primary focus on digital and data-driven analysis through automated processes • Contribute analytical expertise to projects in digital strategy and digital planning • Conduct robust modelling, data analysis, and research to support the digitisation of planning • Build data and analytical pipelines (in Python) to automate key modelling workflows • Develop robust, tested code that meets internal standards • Prepare clear and compelling reports, technical outputs, and presentations for clients • Collaborate with internal specialists across transport, energy, digital and systems modelling • Support delivery of consultancy projects to time, quality and budget • Respond flexibly to evolving project needs across the wider consultancy team