Staff Software Engineer, Backend (Lake Analytics Platform)

- 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.

- Lakehouse Platform Expertise: Proven experience architecting, building, launching, and operating large-scale OLAP systems, lakehouse platforms, or analytical data infrastructure using technologies such as Apache Iceberg, Spark, Snowflake, and cloud-native storage.

- Snowflake Platform Expertise: Hands-on experience with Snowflake or comparable analytical data warehouses, including RBAC, dynamic data masking, warehouse optimization, query profiling, clustering, and cost management.

- Data Platform Architecture: Strong understanding of table formats, schema evolution, partitioning, compaction, query performance, data lifecycle management, observability, and cost optimization for analytical systems.

- Governance and Trust: Experience designing secure, reliable, and governed data platforms, including RBAC/ABAC, data quality, lineage, classification, privacy controls, policy enforcement, and operational compliance.

- Analytics Engineering Foundations: Experience with dbt or similar transformation frameworks, data modeling best practices, testing, documentation, CI/CD, and data quality practices for analytical pipelines.

- Agentic Data Tools: Experience building or shaping semantic layers, self-service analytics platforms, internal data applications, or AI-enabled data tools that improve data accessibility and usability.

- Technical Leadership: Demonstrated ability to set technical direction, lead ambiguous platform initiatives, mentor engineers, and influence roadmaps across teams while staying close to implementation details.

- Collaboration: Strong ability to partner with engineering, analytics, machine learning, BI, product, and infrastructure teams to translate business needs into durable technical solutions.

- Communication Skills: Excellent communication skills, with the ability to clearly articulate technical concepts, tradeoffs, and recommendations to technical and non-technical stakeholders.

- Experience: 8+ years of experience in software engineering, data infrastructure, or data platform engineering, with 2+ years of technical leadership responsibilities.

- Hands-on Leadership: Hands-on experience leading teams to build critical data infrastructure.

- Snowflake / Analytical Warehouses: Hands-on experience with Snowflake or comparable analytical data warehouses, including access control, data masking, query optimization, and cost management.

- Lakehouse and Big Data: Strong experience with Apache Iceberg, Spark, and cloud-native data lake architectures.

- Analytics Engineering: Experience with dbt or equivalent transformation frameworks, including data modeling, testing, documentation, and CI/CD practices.

- Programming Skills: Proficiency in Python, SQL, or JVM-based languages, with a strong emphasis on clean, maintainable, production-quality systems.

- Infrastructure as Code: Familiarity with Terraform or similar automation tools for managing data infrastructure.

- Education: This position requires equivalent practical experience or a Bachelor's degree in a related field.

Employees new to Affirm typically come in at the start of the pay range . Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.

USA base pay range (CA, WA, NY, NJ, CT) per year: $230,000 - $290,000 USA base pay range (all other U.S. states) per year: $204,000 - $264,000