Staff Backend Software Engineer (Lake Analytics Platform)

Role overview

Staff Backend Software Engineer for Affirm’s Lakehouse Platform. You’ll drive long-term technical strategy, design and build platform capabilities, improve governance and access controls, strengthen analytics engineering foundations, and operate systems at scale—optimizing both performance and cost.

Key missions

• Define and execute the long-term technical roadmap for the Lakehouse platform
• Build platform capabilities that make analytical data secure, reliable, discoverable, and easy to use
• Establish lakehouse operational best practices (schema evolution, table maintenance, partitioning, compaction, observability, etc.)
• Lead ambiguous platform initiatives and influence roadmaps across teams while staying hands-on

Responsibilities

• Hands-on technical leadership: lead teams building critical data infrastructure
• Improve analytics engineering foundations (modeling, testing, documentation, CI/CD)
• Shape semantic layers / self-service analytics and internal data applications (potentially AI-enabled)
• Provide governance and trust: secure and governed data platforms with policy enforcement and operational compliance
• Optimize query/performance/cost; collaborate with engineering, analytics, ML, BI, product, and infrastructure
• Mentor engineers and partner cross-functionally to translate business needs into durable solutions

Requirements

• 8+ years software engineering / data infrastructure / data platform engineering
• 2+ years technical leadership responsibilities
• Experience leading teams building critical data infrastructure
• Analytics engineering: dbt (or equivalent) including data modeling, testing, documentation, and CI/CD
• Agentic/self-service data tooling or semantic layer experience
• Infrastructure as Code: Terraform (or similar)
• Strong lakehouse/big data experience: Apache Iceberg and Spark, cloud-native data lake architectures
• Deep knowledge of table formats, schema evolution, partitioning, compaction, query performance, data lifecycle management, observability, and cost optimization
• Programming: strong in Python, SQL, or JVM-based languages; production-quality systems
• Governance & security: RBAC/ABAC, data quality, lineage, classification, privacy controls, policy enforcement
• Strong Snowflake (or comparable warehouse) experience including RBAC, dynamic masking, optimization, query profiling, clustering, and cost management
• Education: equivalent practical experience or a Bachelor’s degree in a related field

Nice to haves

• Experience architecting and operating large-scale OLAP systems and lakehouse platforms using Apache Iceberg, Spark, Snowflake, and cloud-native storage
• Demonstrated influence across multiple teams and strong stakeholder communication