Staff Software Engineer
Streaming Infrastructure
Updated on 8/24/2023

1,001-5,000 employees

Point of sale installment loans for consumers
Company Overview
Affirm seeks to deliver honest financial products—to improve lives. The company operates a buy-now-pay-later platform for consumers at the point of sale.
Remote in USA
Experience Level
Desired Skills
Apache Spark
Data Analysis
Data Science
Apache Beam
Apache Flink
Data & Analytics
DevOps & Infrastructure
Software Engineering
  • 7+ years of industry experience in building large scale production systems
  • Experience building and owning large-scale stream processing systems
  • Experience building and operating robust and highly available infrastructure
  • Working knowledge of Relational and NoSQL databases
  • Experience working with Data Warehouse solutions
  • Experience with industry standard stream processing frameworks like Spark, Samza, Flink, Beam etc
  • Experience leading technical projects and mentoring junior engineers
  • Exceptionally collaborative with a history of delivering complex technical projects and working closely with stakeholders
  • BS, MS or PhD in Computer Science, Engineering or a related technical field
  • Architect stream processing features to execute on our ambitious event driven ecosystem
  • Develop robust, well-instrumented stream processing data pipelines that can scale to handle Affirm's future growth and adhere to strict SLAs
  • 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 stream processing frameworks and services which will be used by other engineering teams at Affirm to manage billions of dollars in loans and power and customize user experience
  • Improve the reliability and efficiency of our core data processing systems
  • Develop data processing systems using Apache Flink, Beam and Spark
  • Work cross-functionally with various engineering, data science and analytics teams to identify and execute on new opportunities in data-infrastructure