Staff Software Engineer (Streaming Infrastructure)
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 Infra team at Affirm is responsible for all data computation and drives the strategy for event driven architecture, stream processing, data exploration, discovery and validation. We leverage existing open source technologies like Kafka, Spark, Flink, Beam, Map Reduce and also build our own as needed. As a member of our team you would spend time identifying and executing on new use cases of stream processing, data infrastructure, designing and scaling our existing infrastructure and working closely with other teams to promote the optimal use of data at the company.
What you'll do
* 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.
What we look for
* 6+ 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.
Location - Remote Canada
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 have a simple and transparent remote-first grade-based compensation structure. Offer amounts within the range are based on a number of factors including but not limited to job-related skills, experience, and relevant education or training. Across the broader organization, certain roles are eligible for equity awards upon hire, promotion, tenure milestones and for performance.
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.