SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Imprint is a fintech company that modernizes co-branded credit card programs for major brands like Crate & Barrel, Rakuten, Booking.com, H-E-B, and Shell. The company combines payments infrastructure, intelligent underwriting, and customer data to create personalized financial experiences without requiring partners to become banks. Backed by top-tier VCs including Kleiner Perkins, Thrive Capital, Ribbit, and Khosla Ventures, Imprint is addressing a $300+ billion market of legacy co-branded card systems.
The Data Engineering team builds the foundation enabling company-wide data understanding, trust, and usage. As Data Engineering Manager, you will lead this team through a critical scaling phase, building a high-performing organization while creating a platform that empowers domain experts across the company to contribute analytics engineering independently.
Key responsibilities include: building and developing an exceptional data engineering team through hiring, coaching, and growing senior technical leaders; setting technical direction for data architecture and engineering practices; partnering across Infrastructure, Analytics, Data Science, Product, Engineering, and Finance teams; and shaping the broader Data organization alongside the Head of Data.
Priorities include making Imprint's data legible through a clear, consistent enterprise data warehouse; establishing strong principles and patterns for dbt and Snowflake development; scaling analytics engineering beyond the core team by creating a development model where domain experts can safely contribute high-quality models; and building the platform and tooling that makes distributed contribution fast and safe.
You will bring deep technical judgment without carrying an individual contributor roadmap. You should be comfortable diving into details, challenging assumptions, and helping the team reason from first principles about data modeling and architecture. The role emphasizes using AI to increase both speed and quality across the analytics engineering lifecycle.
