Senior Staff Software Engineer, Backend (Data and Storage Services)

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.

Affirm's engineering team is building a large-scale, highly-available, and global infrastructure that is shared across multiple financial products. Ensuring that our infrastructure is accessible to all engineers is critical to the success of the business. We pride ourselves on our culture across engineering of engaging in thorough technical design review, operational excellence, and capable incident response and analysis.

The Data and Storage Services team is responsible for handling all of affirm's Data (OLAP and OLTP) requirements and encompasses the entire range from critical online checkout databases all the way to our Batch Orchestration, Streaming Infrastructure, Event‑Driven Frameworks, BI and analytics tools and systems. Our mission is to provide trustworthy, intuitive, and cost‑efficient solutions for affirmers to secure, store, analyze, and transform data at exceptional scale.

The Data Services organization encompasses the Lake Analytics Platform and Analytics Engineering teams. Our platform powers affirm's analytical data ecosystem — from the lakehouse and query infrastructure that stores and serves data at scale, to the transformation and modeling layers that make data trustworthy and accessible to the business. We are responsible for Snowflake, FiveTran, Atlan, MonteCarlo, dbt, data governance, privacy controls, and the tooling that enables self‑service analytics with an AI focused mindset across the company.

What you’ll do

• Architect and evolve affirm's lakehouse analytics platform, driving strategy around Snowflake, Apache Iceberg, and Spark to deliver scalable, high‑performance analytical infrastructure.
• Design and implement robust role‑based access control (RBAC) and dynamic data masking policies in Snowflake, ensuring data access is secure, compliant, and auditable across the organization.
• Lead the technical direction of analytics engineering practices, including data modeling, transformation pipelines (dbt), and data quality frameworks that enable trustworthy, self‑service analytics.
• Drive data governance and privacy engineering initiatives, leveraging tools like Atlan to manage data cataloging, lineage, classification, and policy enforcement.
• Identify and execute cost‑optimization strategies across affirm's analytical compute and storage footprint, including Snowflake warehouse tuning, query optimization, and efficient data lifecycle management.
• Collaborate with product engineering, data science, and business intelligence teams to understand their data needs and provide continuous guidance on design, architecture, and best practices.
• Establish and champion best practices for lakehouse operations at scale, including schema evolution, table maintenance, partitioning strategies, and observability.
• Stay ahead of industry trends in analytical data platforms, data governance, and privacy technologies, and identify opportunities to innovate and improve our data offerings.
• Mentor engineers across the Lake Analytics Platform and Analytics Engineering teams, providing guidance on emerging technologies, development practices, and fostering a culture of technical excellence.
• Participate in an on‑call rotation and collaborate with other teams such as SRE to resolve production issues.

What we look for

• Architect and implement core components of affirm's lakehouse analytics platform, with a focus on scalability, governance, and reliability.
• Snowflake expertise: Deep knowledge of Snowflake to architect RBAC models, dynamic data masking, warehouse optimization, and multi-cluster compute strategies, including query profiling, micro‑partitioning, clustering, materialized views, and cost attribution.
• Analytics engineering experience: Drive the technical strategy for data modeling and transformation using dbt, including testing frameworks, documentation standards, and CI/CD for data pipelines.
• Data governance & privacy: Design and operate data governance frameworks using tools such as Atlan for cataloging, lineage tracking, classification, and automated privacy policy enforcement.
• Lakehouse architecture: Tackle challenges of large‑scale analytical data systems, including Apache Iceberg table management, schema evolution, storage optimization, and integration with Spark and Snowflake.
• Collaboration: Work closely with product managers, software engineers, and analysts to translate business requirements into technical solutions, and with fellow engineers to deliver high‑quality data infrastructure.
• Mentorship: Guide and mentor junior and senior engineers, sharing expertise and fostering a culture of technical excellence.
• Innovation: Stay ahead of the curve by researching and experimenting with emerging technologies and trends in the lakehouse, data governance, and analytics engineering space.

Qualifications

• 10+ years of experience in software engineering or data engineering, with a proven track record of delivering complex data platform solutions that improve accessibility, performance, and governance of analytics infrastructure.
• 6+ years of hands‑on experience with Snowflake or comparable analytical data warehouses, including RBAC design, data masking, query optimization, and cost management.
• Strong experience with Apache Iceberg, Spark, and cloud‑native data lake architectures on AWS (S3, EKS).
• Experience with dbt or equivalent transformation frameworks, including data modeling best practices, testing, and CI/CD for data pipelines.
• Exceptional problem‑solving and analytical skills, with the ability to identify and resolve complex technical challenges and establish long‑lasting solutions and processes.
• Proficiency in Python and SQL, with a strong emphasis on clean, maintainable code. Experience with Kotlin or Go is a plus.
• Demonstrated leadership and mentorship skills, with the ability to inspire and guide others and work cross‑functionally to influence roadmaps.
• Passion for engaging with the data engineering community and contributing to open‑source projects.
• Familiarity with automating infrastructure using tools such as Terraform for managing data infrastructure.
• Excellent communication and interpersonal skills, with the ability to clearly articulate technical ideas to both technical and non‑technical audiences.

Compensation & Equity

Pay Grade – R

Equity Grade – 9

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.

Base pay range per year: $206,000 – 256,000

Benefits

• Health care coverage – affirm covers all premiums for all levels of coverage for you and your dependents.
• Flexible Spending Wallets – generous stipends for spending on technology, food, lifestyle, and family‑forming expenses.
• Time off – competitive vacation and holiday schedules allowing you to take time off to rest and recharge.
• ESPP – an employee stock purchase plan enabling you to buy shares of affirm at a discount.

We believe it’s on us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.

For U.S. positions that could be performed in Los Angeles or San Francisco, pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, affirm will consider for employment qualified applicants with arrest and conviction records.