Job Title

Staff Software Engineer, Backend (Streaming) Join to apply for the Staff Software Engineer, Backend (Streaming) role at Affirm 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 at Affirm 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 existing open source technologies like Kafka, Flink, Spark and also build our own as needed to design and build real-time data processing pipelines that handle massive volumes of data across our distributed systems. Responsibilities Design and build data infrastructure systems, services and tools to handle new Affirm products and business requirements that securely scale over millions of users and their transactions. Build and optimize high-throughput, low-latency data pipelines for critical business applications. Build frameworks and services which will be used by other engineering teams at Affirm to manage billions of dollars in loans and power customer experiences. Help support the Streaming team that forms the backbone for several online and offline workloads at Affirm. Improve the reliability and efficiency of the Data Platform at scale and high availability. Collaborate with other teams at Affirm, including ML and Analytics, to deliver streaming solutions based on various use-cases and best practices. Monitor, troubleshoot, and maintain production streaming systems, ensuring high availability and reliability of data systems. What We Look For 8+ years of industry experience in 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 and highly available infrastructure. Experience working 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 like Spark, Samza, Flink, Beam etc. Hands-on experience with Kafka Connect and Kafka Schema Registry components, understanding their underlying functionality and implementation. Solid programming skills in Python, Java or Kotlin. Experience with Apache Iceberg table format, CDC (Change Data Capture) 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 and Compensation Location - Remote Canada Pay Grade - P Equity Grade - 7 Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company). CAN base pay range per year: $178,000 - $228,000 Affirm is proud to be a remote-first company. The majority of roles are remote and you can work almost anywhere within the country of employment. Some roles may require periodic on-site work. Benefits Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents Flexible Spending Wallets - stipends for Technology, Food, Lifestyle needs, and family forming expenses Time off - vacation and holiday schedules to rest and recharge ESPP - Employee stock purchase plan allowing you to buy shares at a discount Were committed to an inclusive interview experience for all and provide reasonable accommodations during the hiring process. By clicking 'Submit Application,' you acknowledge Affirm's Global Candidate Privacy Notice and consent to the collection, processing, and storage of your personal information as described therein. #J-18808-Ljbffr