Data Engineering Manager

About this role

WHO WE ARE

Imprint helps the world's best brands grow the lifetime value of their customers. We started with co-branded credit cards and rebuilt them to be smarter, more rewarding, and brand-first. We partner with companies like Crate & Barrel, Rakuten, Booking.com http://Booking.com, H-E-B, Fetch, and Shell to launch modern credit programs that deepen loyalty, unlock savings, and drive growth. But the card is just the beginning. We combine advanced payments infrastructure, intelligent underwriting, and deep customer data to create delightful and personalized experiences for members as well as efficient and profitable relationships for our brand partners. Our robust technology and world-class operations allow us and our brand partners to offer powerful financial products without becoming a bank.

In the U.S., co-branded cards alone account for over $300 billion in annual spend, and most still run on decades-old legacy bank systems. Imprint is the modern alternative: flexible, embeddable, and built for how people actually pay today. Backed by Kleiner Perkins, Thrive Capital, Ribbit, and Khosla Ventures, we're building a world-class team to redefine how people pay and how brands grow. If you want to move fast, solve hard problems, and own real outcomes, we want to meet you.

The Data Engineering team at Imprint builds the foundation that enables teams and systems across the company to understand, trust, and use data. We own the core data platform, development patterns, and standards behind Imprint's enterprise data warehouse, partnering closely with Infrastructure and with teams across the business.

As Imprint scales, one of our biggest data challenges is turning an increasingly complex business into data that is consistent, understandable, and easy to use by machines or people. Doing that well requires great analytics engineering: clear models of the business, strong definitions and abstractions, and systems that make high-quality data development repeatable.

We don't believe the answer is simply adding more Data Engineers. We want to build a platform that gives domain experts across Imprint the tools and guardrails to contribute safely and independently—allowing us to dramatically increase the pace of analytics engineering without making Data Engineering a bottleneck.

We're looking for a Data Engineering Manager to lead this next phase of our Data Engineering organization. You will build and develop a high-performing team, raise the bar for analytics engineering, and create the platform and development model that allow high-quality data development to scale across Imprint.

This is a people leadership role for someone with deep technical judgment. You won't carry an individual contributor roadmap, but you should be comfortable getting into the details, challenging assumptions, and helping the team reason from first principles about data modeling, architecture, and engineering practices.

WHAT YOU'LL DO

- Build and lead an exceptional Data Engineering team. Hire great engineers, coach and develop the team, grow senior technical leaders, set a high performance bar, and create a culture of ownership and continuous improvement.

- Use AI to increase both speed and quality. Develop and implement a point of view on how AI can improve the full analytics engineering lifecycle, from stakeholder engagement, team management, to design and development.

- Set the technical direction for Data Engineering. Bring strong opinions grounded in experience while encouraging first-principles thinking. Guide architectural decisions and ensure the team builds systems that are simple, scalable, reliable, and appropriate for the problems we're solving.

- Partner across the company. Work closely with Infrastructure, Analytics, Data Science, Product, Engineering, Finance, and other teams to understand how the business works, define clear ownership, and build a data ecosystem that enables teams to move faster and make better decisions.

- Shape the future of the Data organization. Partner with the Head of Data on organizational design, hiring, roadmap prioritization, and the investments needed to support Imprint's continued growth.

Our Data Engineering priorities include:

- Make Imprint's data legible. Lead the evolution of our enterprise data warehouse into a clear, consistent, and trusted representation of how Imprint's business works. Ensure important business concepts have well-designed models, clear definitions, and intuitive relationships that make our data easier to understand and use.

- Raise the bar for analytics engineering. Establish strong principles and patterns for data modeling, transformation, testing, documentation, and ownership across our dbt and Snowflake environment. Create the technical standards that allow our data models to remain coherent as the business grows more complex.

- Scale analytics engineering beyond the Data Engineering team. Create a development model where domain experts can turn their knowledge into high-quality, reusable data models without Data Engineering sitting in the critical path. Define the right boundaries, abstractions, review processes, and ownership model to make distributed contribution successful.

- Build the platform that makes this possible. Partner closely with Infrastructure to create the tooling, workflows, and guardrails that make contributing to our enterprise data warehouse fast and safe. Make the right way the easiest way.

WHAT WE LOOK FOR

- Experience introducing AI-assisted workflows at scale.

- Experience in a high-growth startup, fintech, or another environment where data correctness and reliability are critical.

- A track record of building and leading high-performing data or analytics engineering teams, including hiring exceptional talent, developing engineers, managing performance, and growing senior technical leaders.

- Deep expertise in analytics engineering with dbt, with experience turning complex business d

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