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Staff Data Engineer

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, Inc. proudly includes Affirm, PayBright, and Returnly. 


What you'll do

  • Help shape the technical direction of a domain within the Data@ organization.
  • Build a reliable and scalable single source of truth core model data product for internal and external analytics and enable self-service.
  • Partner with product, data analyst, engineering teams and other data engineers to translate business requirements into data models with measurable transformation quality under SLA.
  • Develop and automate large-scale, high-performance data processing systems and visualization to ensure reliability and meet critical business requirements.
  • Lead data engineering projects, and overall strategy for data governance, security, privacy, quality, and retention.
  • Mentor data engineers and continue promoting data engineering and analytics tooling & standards.


What we look for

  • 7+ years of experience in data modeling, data architecture, and other areas directly relevant to data engineering.
  • Technical leadership; capable of handling mentorship, cross functional project execution, and solid individual contributions.
  • Advanced SQL, ETL pipelines, Data modeling & design, SQL performance tuning in OLAP and Data Warehouse/Data Lake environments
  • Proficiency with programming languages (e.g. Python) and Data Warehouse technologies (NoSQL, logging, columnar, Snowflake, etc.), Big Data technologies (e.g Hadoop, Spark, etc.), analytics (Looker, Tableau, etc.)
  • Familiar with data governance frameworks and Agile methodology
  • Excellent written and verbal communication and interpersonal skills; able to effectively collaborate with technical and business partners.
  • Eager to learn new things and have a growth mindset.
  • BS/MS degrees in Computer Science, Engineering, or a related technical field.


Location - Remote U.S.


Affirm is proud to be a remote-first company ! The majority of our roles are remote and can be located anywhere in the U.S. and Canada (with the exception of the U.S. Territories, Quebec, Yukon, Nunavut, and the Northwest Territories) unless the job indicates a different global location. We are currently building operations in Spain, Poland, and Australia.  Employees in remote roles have the option of working remotely or from an Affirm office in their country of hire, and may occasionally travel to an Affirm office or elsewhere for required meetings or team-building events. Our offices in Chicago, New York, Pittsburgh, Salt Lake City, San Francisco and Toronto will remain operational and accessible for anyone to use on a voluntary basis, subject to local COVID-19 guidelines.


If you got this far, we hope you're feeling excited about this role. Even if you don't feel you meet every single requirement, we still encourage you to apply. We're eager to meet people who believe in Affirm's mission and can contribute to our team in a variety of ways—not just candidates who check all the boxes. Inclusivity:At Affirm, People Come First is one of our core values, and that’s why diversity and inclusion are vital to our priorities as an equal opportunity employer. You can read about our D&I program here and our progress thus far in our 2020 DEI Report .

We also 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.


By clicking "Submit Application," you acknowledge that you have read the Affirm Employment Privacy Policy , or the Affirm Employment Privacy Notice (EU) for applicants applying from the European Union, and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.

Company

Affirm

Location

USRemote

Job type

Full-Time

Category

Data Engineering

Tags

Big DataEngineeringETLHadoop
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