Focus Financial Partners
BI & Data Engineer
St. Louis, MOmidAdded today
About this role
Build and maintain Focus's data consumption layer by developing Power BI reports, administering the BI platform, and creating interactive data applications (Streamlit and React+FastAPI) on Snowflake. This hybrid role combines traditional BI work with full-stack application development, leveraging AI-assisted tools to deliver usable data experiences for business stakeholders.
What you'll do
- Design and maintain Power BI reports and dashboards on Snowflake semantic models with focus on accuracy and performance
- Administer Power BI tenant including workspace governance, row-level security, capacity management, and gateway configuration
- Develop interactive data applications using Streamlit in Snowflake and custom React+FastAPI applications via Snowpark Container Services
- Collaborate with data and analytics engineers to consume Snowflake semantic models and translate business requirements into reporting solutions
- Monitor and optimize performance across Power BI reports and Snowflake applications while managing access controls
- Manage full application lifecycle including version control, CI/CD pipelines, testing, and release management
What they're looking for
- Power BI development (DAX, data modeling, report design)
- Power BI administration (workspace governance, RLS, gateways, capacity planning)
- Snowflake and DirectQuery/Import connectivity
- Streamlit and Python application development
- React and FastAPI (full-stack web development)
- Git/GitHub and CI/CD practices
- AI-assisted development tools (GitHub Copilot)
- SQL and semantic data modeling
Benefits
- Hybrid arrangement with 3 days/week onsite in St. Louis
Likely interview questions
- Walk us through your experience building and administering Power BI across an enterprise environment—specifically how you've handled workspace governance and row-level security at scale.
- Tell us about a time you optimized DirectQuery performance in Power BI connected to a cloud data warehouse. What challenges did you encounter and how did you solve them?
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