Remote Data Engineer

Our StoryParker's mission is to increase the number of financially independent people in the e-commerce space. We aim to achieve this by creating tools that allow independent business owners to scale their operations profitably. Our product suite offers Card & Bill Pay, Banking, and Analytics designed to help e-commerce merchants grow and maximize their profitability.We are growing rapidly — in the last several years, we've processed over a billion in spend on Parker's platform. To support this demand, we're looking to expand our team quickly. Our investors include Solomon Hykes (founder of Docker), Paul Buchheit (founder of Gmail), Paul Graham (founder of Y Combinator), Robert Leshner (founder of compound.finance), among others. We've raised over $40M from top-tier fintech investors.The RoleWe are seeking a highly skilled Data Engineer to develop new features and expand existing features of our products while maintaining and improving our data pipeline system. This includes monitoring, ensuring data quality, and providing accurate data to inform team decisions and customer performance analytics. The role involves building features, performing maintenance, and continuously enhancing the system.The ideal candidate will have strong proficiency in Python and SQL, with 2-3 years of experience specifically in Data Engineering. This person also has experience with the modern data engineering technologies listed below and demonstrates a strong sense of ownership, initiative, and collaboration. A background in fintech, e-commerce, or the AI startup space is a bonus but not required. The role offers significant ownership, freedom to lead with curiosity, explore, and innovate.This position is available remotely anywhere in the US, or hybrid based in NYC at the Parker HQ. We offer a competitive salary range of $100K-$120K, with an option grant. If you resonate with this job description and our mission, we invite you to apply for this exciting opportunity at Parker.What You’ll Do

• Collaborate with other teams to design and develop new features and expand existing ones in our data pipeline system.
• Improve data quality and monitoring by learning the existing system and implementing enhancements.
• Establish standard processes for data ingestion and management.
• Monitor data quality and ensure accuracy for team decisions and customer performance analytics.
• Build and maintain data infrastructure and pipelines.