Senior Business Intelligence Engineer

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
• 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