Staff Software Engineer, Backend (Lake Analytics Platform)

Remote from: USA
Salary, yearly, USD: 204,000 - 290,000
Department: Software Engineering
Employment type: Full Time,
Job posted: 14 Jul 2026
Apply before: 14 Aug 2026
Experience level: Senior
Views / Applies: 43 / 1

About company

About Affirm

Buy now. Pay later. Simple and transparent financing.

Financial Services
2012

Actively Hiring

AI Summary

Affirm is seeking a Staff Software Engineer to lead their Lakehouse Platform, focusing on Apache Iceberg, Spark, and Snowflake. This role involves defining technical strategy, designing scalable analytical data storage, strengthening governance, and collaborating across teams. The ideal candidate has deep expertise in lakehouse architecture and large-scale OLAP systems. This is a high-impact opportunity to shape Affirm's data infrastructure at scale, with a competitive salary and benefits.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight The role requires advanced technical leadership in complex lakehouse platforms and cross-functional collaboration, necessitating deep expertise and strategic influence, making it a high-difficulty position.

Salary Analysis

AI Insight The offered salary range of $204,000 to $290,000 is competitive for a Staff Software Engineer in the data platform space, aligning well with market rates for high-impact roles in fintech. The median of $247,000 is strong, reflecting the level of expertise required.

Core Skills Required

Apache Iceberg Snowflake Apache Spark Data Governance Lakehouse Architecture Cloud Infrastructure Performance Optimization Cross-functional Collaboration Technical Leadership Data Engineering

Cover Letter Sample

Dear Hiring Manager,

I am writing to express my strong interest in the Staff Software Engineer, Backend (Lake Analytics Platform) role at Affirm. With over 8 years of experience architecting large-scale data platforms, I have deep expertise in Apache Iceberg, Snowflake, and Spark, directly aligning with the key technologies for this role.

At my previous company, I led the migration of our analytical storage to a lakehouse architecture, resulting in 40% cost reduction and improved query performance. I have a proven track record of defining technical strategy, strengthening data governance, and fostering cross-functional collaboration.

I am excited about the opportunity to drive Affirm's lakehouse platform and contribute to an inclusive engineering culture. Thank you for your consideration.

Sincerely,
[Your Name]

Sample Interview Questions

Describe a time you designed a scalable lakehouse platform. What technologies did you use and how did you balance performance, cost, and governance?

I led the design of a lakehouse platform using Apache Iceberg, Spark, and Snowflake. We implemented partitioning and compaction strategies to optimize query performance, used lifecycle management to reduce costs, and enforced RBAC and data masking for governance. The platform scaled to support over 500 TB of data with 99.9% availability.

How do you approach data governance in a lakehouse environment?

I prioritize fine-grained access control using RBAC, dynamic data masking, and auditing. I implement cataloging and lineage tools like Apache Atlas or DataHub for discoverability. Regular reviews and automated policies ensure compliance, and I collaborate with legal and security teams to align with regulations.

Can you walk through a challenging incident involving data platform performance and how you resolved it?

We experienced slow queries due to small files in Iceberg. I implemented automatic compaction and optimized partitioning keys. I also added observability with metrics on query latency and storage usage. The changes reduced query time by 60% and improved overall system reliability.

How do you stay current with industry trends in data infrastructure, and how have you brought innovations to your team?

I regularly read papers and attend conferences like Spark+AI Summit. I introduced Iceberg’s time-travel feature for data recovery and implemented a semantic layer to simplify analytics. I also started a tech talk series to share knowledge across the team.

Describe a situation where you had to influence technical strategy across multiple teams. How did you build consensus?

I proposed adopting Iceberg as our table format, but teams were hesitant due to migration effort. I ran a pilot demonstrating 30% cost savings and faster queries. I showed clear benchmarks and presented trade-offs in a cross-functional meeting, gaining buy-in from engineering, analytics, and finance.

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 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 Affirm’s data infrastructure across OLTP and OLAP systems, spanning critical online checkout databases, batch orchestration, streaming infrastructure, event-driven frameworks, BI, analytics tooling, large-scale data platforms, and agentic data tools such as semantic layers and internal platform data applications. Our mission is to provide trustworthy, intuitive, and cost-efficient solutions for Affirmers to secure, store, analyze, and transform data at exceptional scale.

This role will focus on Affirm’s Lakehouse Platform, including Apache Iceberg as a foundational technology for scalable analytical data storage, table management, schema evolution, and interoperability across compute engines such as Spark and Snowflake.

What You’ll Do

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.

What We Look For

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.

Qualifications

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.

Base Pay Grade – P

Equity Grade – 13

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 is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)

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

#LI-Remote

Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.

We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:

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, various Lifestyle needs, 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.

By clicking “Submit Application,” you acknowledge that you have read Affirm’s Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.

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