Staff Data Engineer
Imprint
| Company | Imprint |
| Category | Engineering |
| Location | San Francisco |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | USD 200k–235k |
| Posted | 3 Jun 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
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 OPPORTUNITY
- Architect and scale Imprint's core data platform, including Snowflake, dbt Cloud, and real-time CDC pipelines, building infrastructure designed for the next decade of growth
- Design secure, compliant partner data delivery systems via Snowflake shares, S3/SFTP integrations, and Marketplace listings that support Imprint's expanding partner ecosystem
- Build mission-critical financial reporting pipelines with exceptional accuracy, reliability, and auditability
- Establish company-wide data standards for modeling, lineage, contracts, and orchestration, championing data reliability, observability, and trust across all systems
- Lead the adoption of AI-assisted development workflows and evaluate how AI tooling (Claude, Copilot, Cursor) can accelerate pipeline development, testing, documentation, and data quality monitoring across the team
- Elevate engineering practices across Analytics, Data, and Engineering teams through architecture reviews, reusable frameworks, mentorship, and hands-on technical guidance
- Make strategic technology decisions that balance innovation with pragmatism, influencing technical and business leadership across multiple departments
YOUR PROFILE
Required
- 10+ years of experience in data engineering or related fields, with proven ownership of platform-level architecture and strategy
- Deep expertise in Snowflake, dbt Cloud, Change Data Capture frameworks, orchestration tools (Airflow, dbt Cloud), and reverse ETL
- Strong background in external data sharing and partner integrations, including Snowflake data shares, S3/SFTP pipelines, and Marketplace listings
- Proven ability to design and implement data governance and observability systems: data contracts, lineage tracking, anomaly detection, and automated monitoring
- Strong engineering skills in SQL and Python, with emphasis on testing, CI/CD, and maintainability in complex data systems
- Active experience with AI-assisted development tools integrated into engineering workflows, with an eye toward scaling those practices across a team
- Reputation as a mentor and technical authority who elevates the quality and rigor of the people and teams around them
- Exceptional ability to communicate and influence across technical and business leadership, translating platform decisions into business impact
Nice to Have
- Experience in fintech, payments, lending, or regulated financial environments where data accuracy and compliance are non-negotiable
- Experience building or scaling data infrastructure at a high-growth startup from early stage through rapid expansion
- Familiarity with agentic AI patterns and how they apply to data
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