Analytics Engineer

• Influence technical strategy: Define and drive the long-term technical roadmap for Affirm’s Lakehouse Platform across Apache Iceberg, Spark, Snowflake, and cloud-native storage, balancing scalability, reliability, governance, performance, and cost.
• Design and develop: Architect and implement platform capabilities that make analytical data secure, trustworthy, discoverable, and easy to use across Affirm’s engineering, analytics, machine learning, and business teams.
• Strengthen governance and access controls: Design and operate secure, auditable data access capabilities across Snowflake and the lakehouse platform, including RBAC, dynamic data masking, cataloging, lineage, classification, and privacy policy enforcement.
• Improve analytics engineering foundations: Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics.
• Operate at scale: Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations.
• Optimize performance and cost: Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency.
• Collaborate cross-functionally: Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture.
• Innovate: Stay ahead of industry trends in lakehouse architecture, open table formats, analytical compute engines, data governance, privacy engineering, semantic layers, agentic data tools, and AI-ready data infrastructure.
• Build teams: Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning.

Staff Software Engineer, Backend – Lake Analytics Platform

• 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

• 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.

Senior Software Engineer, Backend – Lake Analytics Platform

• Design and implement a formal business data layer in dbt, ensuring that data from multiple sources is centralized, reconciled, and serves as a single source of truth for GTM metrics.
• Collaborate with stakeholders within the GTM Operations team and Finance to define, document, and maintain alignment on key business metrics and data definitions.
• Develop and maintain automated data reconciliation and quality checks to proactively identify and resolve discrepancies, ensuring accuracy and consistency of critical reports and dashboards.
• Mentor and guide members of the data science and analytics team, establishing and enforcing best practices around data modeling, testing, documentation, and code review.
• Lead investigations into complex data anomalies, conduct root cause analysis, and communicate findings and solutions effectively to both technical and non-technical audiences.

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Staff Software Engineer, Backend – Lake Analytics Platform

Analytics Engineer

Senior Software Engineer, Backend – Lake Analytics Platform

Staff Analytics Engineer