Senior Business Intelligence Engineer

About this role

WHO WE ARE

Imprint is building a platform that 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 predict what each customer will do next and act on it, so brands can offer powerful financial products without becoming a bank.

Co-branded cards alone account for over $300 billion in U.S. annual spend, and most still run on legacy bank rails. 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.

As a Senior BI Engineer, you will own the design, development, and delivery of data products that power business decisions across Imprint. This is a high-impact individual contributor role embedded at the intersection of data engineering and analytics — with AI as a core multiplier in how you work.

You will partner closely with teams across Engineering, Product, Finance, Marketing, and Operations to translate complex business questions into reliable, performant, and scalable BI solutions — from data modeling and pipeline development to dashboards and self-serve analytics infrastructure. You will leverage AI-assisted development tools (Claude, Codex, Cursor, etc.) to accelerate implementation, allowing you to focus your energy on the strategic thinking, problem framing, and stakeholder partnership that AI cannot replace.

This role blends technical depth with strong business judgment, and is best suited for someone who can move fluidly between writing production-grade SQL, architecting semantic layers, and sitting in a room with stakeholders to define what "good" looks like.

What Success Looks Like in the First 90 Days

- Delivered at least one high-priority BI initiative end-to-end, from data model to stakeholder-facing dashboard

- Built strong working relationships with key cross-functional stakeholders to understand data needs and priorities

- Identified and addressed at least one significant gap in data reliability, model coverage, or reporting fidelity

- Established or meaningfully improved documentation and discoverability standards for existing BI assets

- Demonstrated effective use of AI-assisted workflows to accelerate delivery — using AI for implementation (SQL generation, model scaffolding, documentation) while applying human judgment to design, scoping, and quality assurance

- Demonstrated clear judgment in prioritizing requests based on business impact and technical feasibility

Responsibilities

- Design, build, and maintain scalable data models, semantic layers, and data visualizations that serve business-critical reporting needs

- Partner with stakeholders across Engineering, Product, Finance, Marketing, and Operations to understand data requirements and translate them into reliable data solutions

- Own data quality, documentation, and governance practices for BI assets — ensuring dashboards and models are accurate, trustworthy, and maintainable

- Build and maintain dbt models to support consistent, reusable data definitions

- Leverage AI-assisted development tools to accelerate model development, dashboard scaffolding, and documentation — treating AI as a productivity multiplier while owning the analytical design and validation

- Develop and enforce best practices for data model development, such as naming conventions and testing standards

- Identify and resolve performance bottlenecks in queries, pipelines, and reporting layers

- Enable self-serve analytics by building machine-legible, intuitive data products that reduce ad hoc request volume

- Use data to surface insights proactively — not just respond to requests, but identify gaps and opportunities in the business

- Continuously evaluate and adopt emerging AI tooling to improve team velocity — contribute to defining how the BI team integrates AI into its standard workflows

- Contribute to the broader data team's roadmap, tooling decisions, and infrastructure

Qualifications

Required

- Demonstrated experience designing and delivering production-grade solutions in a complex, high-scale data environment

- Deep proficiency in SQL and data modeling Strong SQL comprehension and data modeling skills — ability to read, validate, and direct complex queries is more important than raw writing speed given AI-assisted workflows

- Experience with a modern data stack (e.g. Snowflake/Databricks, dbt, Sigma/Looker, etc.)

- Strong ability to work directly with stakeholders — translating ambiguous business questions into clear, scoped data solutions

- Track record of building data products that are adopted and trusted by non-technical users

- Demonstrated experience building with AI tools (Claude, Codex, Copilot, Cursor, or similar) — not just awareness, but active integration into daily analytical and engineering workflows

Nice to Have

- Experience in fintech, payments, lending, or regulated financial environments

- Familiarity with data orchestration tools (e.g., Airflow)

- Experience building or scaling BI infrastructure in a high-growth startup environment

- Experience defining or implementing AI-augmented analytics workflows at a team level — e.g., AI-assisted code review, automated documentation, prompt-driven data exploration

- Familiarity with agentic AI patterns (MCP, tool-use, context management) and how they apply to data workflows

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