Staff Software Engineer, Backend (Streaming)

Staff Software Engineer, Backend (Streaming)

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.

The Streaming team forms the backbone of several online and offline workloads and drives the strategy for event-driven architecture, stream processing, replication, data exploration, discovery and validation. We leverage open source technologies like Kafka, Flink, Spark and build our own as needed to design and build real-time data processing pipelines that handle massive volumes of data across distributed systems.

As a member of the Streaming team, you would identify and execute on new streaming data infrastructure use cases, design and scale infrastructure, and collaborate with other teams to promote optimal data use across the company. The role requires deep expertise in stream processing technologies and a passion for building scalable, fault-tolerant data infrastructure.

• Design and build data infrastructure systems, services and tools to support new Affirm products and business requirements, securely scaling over millions of users and transactions.
• Build and optimize high-throughput, low-latency data pipelines for critical business applications.
• Develop frameworks and services to be used by other engineering teams to manage billions of dollars in loans and power customer experiences.
• Support the Streaming team that underpins multiple online and offline workloads at Affirm.
• Improve reliability and efficiency of the data platform at scale with high availability.
• Collaborate with ML and Analytics teams to deliver streaming solutions across various use-cases and best practices.
• Monitor, troubleshoot, and maintain production streaming systems to ensure data reliability.

• 8+ years of industry experience building large-scale production systems.
• Strong hands-on experience with Apache Kafka or similar streaming solutions for large-scale event streaming and message queuing.
• Experience building and operating robust, highly available infrastructure.
• Experience with data platforms like Snowflake, Glue or Databricks is a plus.
• Experience with Confluent Platform (Schema Registry, Tableflow) is a strong plus.
• Expertise with at least one stream processing framework (Spark, Samza, Flink, Beam, etc.).
• Hands-on experience with Kafka Connect and Kafka Schema Registry components, understanding their underlying functionality.
• Solid programming skills in Python, Java or Kotlin.
• Experience with Apache Iceberg table format and CDC tools is a strong plus.
• Knowledge of relational and NoSQL databases is a plus.
• Experience leading technical projects and mentoring junior engineers.
• Exceptionally collaborative with a history of delivering complex technical projects and working closely with stakeholders.

Location: Remote Canada

Pay Grade: P

Equity Grade: 7

Base pay range (Canada): CAN$178,000 – CAN$228,000 per year. Compensation may include health, wellness and tech stipends, and benefits. Equity rewards may be offered by Affirm Holdings, Inc. (parent company).

Affirm is remote-first; most roles are remote. Some roles may require working from an office occasionally.

• Health care coverage – Affirm covers premiums for you and dependents
• Flexible Spending Wallets – stipends for technology, food, lifestyle needs, and family forming expenses
• Paid time off – vacation and holidays
• ESPP – Employee Stock Purchase Plan

We’re committed to an inclusive interview experience and can provide reasonable accommodations during the hiring process.

For U.S. positions that could be performed in Los Angeles or San Francisco, Affirm considers qualified applicants with arrest and conviction records as required by local law.

By submitting your application, you acknowledge Affirm's Global Candidate Privacy Notice and consent to the collection, processing, use and storage of your personal information as described therein.

• Mid-Senior level

• Full-time

• Engineering and Information Technology