Senior Business Intelligence Engineer

United States·New Yorkfull-timesenior

OtherBusiness Intelligence Engineer

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Quick Summary

Overview

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,

Technical Tools

OtherBusiness Intelligence Engineer

What We Offer

~2 min read

✓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

✓Competitive compensation and equity packages

✓Leading configured work computers of your choice

✓Flexible paid time off

✓Fully covered, high-quality healthcare, including fully covered dependent coverage

✓Additional health coverage includes access to One Medical and the option to enroll in an FSA

✓20 weeks of paid parental leave for the primary caregiver and 8 weeks for all new parents

✓Access to industry-leading technology across all of our business units, stemming from our philosophy that we should invest in resources for our team that foster innovation, optimization, and productivity

Responsibilities

~1 min read

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Design, build, and maintain scalable data models, semantic layers, and data visualizations that serve business-critical reporting needs
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Partner with stakeholders across Engineering, Product, Finance, Marketing, and Operations to understand data requirements and translate them into reliable data solutions
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Own data quality, documentation, and governance practices for BI assets — ensuring dashboards and models are accurate, trustworthy, and maintainable
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Build and maintain dbt models to support consistent, reusable data definitions
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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
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Develop and enforce best practices for data model development, such as naming conventions and testing standards
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Identify and resolve performance bottlenecks in queries, pipelines, and reporting layers
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Enable self-serve analytics by building machine-legible, intuitive data products that reduce ad hoc request volume
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Use data to surface insights proactively — not just respond to requests, but identify gaps and opportunities in the business
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Continuously evaluate and adopt emerging AI tooling to improve team velocity — contribute to defining how the BI team integrates AI into its standard workflows
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Contribute to the broader data team's roadmap, tooling decisions, and infrastructure

Requirements

~1 min read

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

~1 min read

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
Exposure to Python or other scripting languages for data transformation or automation
Track record of establishing BI governance or data quality frameworks from the ground up