The Business Intelligence Analyst will own the analytics layer behind our client recognition and incentive programs on RewardStation. This role will model program data, build and maintain the dashboards clients and account teams run on, and use AI tooling as a core part of how you work - to move faster on SQL, QA, documentation, and insight, while staying accountable for what the numbers say. This is a hands-on build role, not a reporting-request queue.
What you'll do- Model the data. Design and maintain data models and semantic layers (star schemas, metric definitions) that make program data reliable and reusable across reports.
- Build the pipelines. Build, version, and document SQL transformations; implement automated data-quality checks and own fixes when something breaks.
- Ship the dashboards. Develop and maintain client-facing and internal dashboards in our BI platform; standardize KPIs for participation, engagement, redemption, and program ROI.
- Work AI-first. Use LLM tools and AI-connected data agents to draft and review SQL, generate first-pass analyses and narrative summaries, and automate recurring reporting. Validate every AI output before it reaches a client.
- Deliver insight. Investigate client and program questions, surface trends, and turn findings into clear recommendations for account, product, and leadership teams.
- Modernize the stack. Contribute to our BI platform migration and modernization: migrate reports, retire legacy artifacts, and improve performance.
- Keep it governed. Maintain the data dictionary and metric documentation; handle client data in line with privacy and security requirements.
What you bring- Proactive ownership. You find the question before it's asked, turn a vague business need into a spec and a shipped result, and drive the work to done. Clear written and verbal communication with non-technical stakeholders is a given. We're hiring a builder, not an order-taker.
- 3-5 years in business intelligence, analytics engineering, or data analysis, ideally in a SaaS or B2B platform environment.
- Advanced SQL (T-SQL required; columnar engines such as DuckDB or Snowflake a plus) and solid data-modeling fundamentals.
- Production experience in at least one modern BI tool - Sisense, Power BI, Tableau, or Looker - including semantic-layer design.
- Working Python for analysis and automation (pandas or similar) and comfort with Git-based workflows.
- Daily, hands-on use of AI assistants (Claude, ChatGPT, Copilot, or similar) in analytics work, with the judgment to catch when they're wrong.
Nice to have- dbt or similar transformation frameworks; ELT patterns on cloud storage (S3) or a cloud warehouse.
- DuckDB / MotherDuck, and exposure to MCP-based or agentic data tooling.
- SSIS or legacy ETL experience, useful for migration work.
- Jira / Confluence; experience in employee recognition, incentives, or HR tech.
Tools you'll useClaude, DuckDB / MotherDuck, Python, Git, MCP-connected data tools, Jira / Confluence, SQL Server.
How we measure success- Dashboards and models are trusted: metric definitions are documented and defect rates fall quarter over quarter.
- Turnaround on client and internal data questions shrinks as AI-assisted workflows and reusable models take hold.
- Migration milestones are delivered on schedule with no loss of client-facing
How we workWe are a small team. We directly communicate with one another, no layers to route around. Decisions get made in the room. We're aggressive about AI leverage - not as a talking point, but because a team this size can't ship what we're shipping otherwise. We're a team of bright, driven professionals, each of whom brings unique perspectives, experience, ideas, and skills to our roles. Our company culture means everything to us - we call it CARE (Collaborative. Adaptable. Respectful. Efficient.) Every member of our team works hard to exemplify these qualities in every aspect of the work we do each day.
