Streamlit Application Development & Ownership
· Design, develop, and ship production Streamlit applications that turn company and GTM requirements into usable data products.
· Own the full lifecycle of the team’s Streamlit apps — build, release, monitoring, and end-to-end support — keeping them reliable as data and requirements evolve.
· Iterate quickly with stakeholders, using AI-assisted development to move from requirement to working app faster than traditional hand-coding.
Advanced Data Modeling & Analytics
· Design, develop, and optimize Snowflake data models to support analytics, applications, and AI-driven products.
· Build scalable, efficient queries and datasets to ensure high-quality, trustworthy data availability for the apps you deliver.
· Use AI tools for development as a core part of daily work — not an occasional add-on — to design, build, test, and maintain applications.
· Apply prompt engineering and modern AI development techniques to accelerate delivery and improve product quality.
Operationalizing AI/ML into Applications
· Integrate the data science team’s model outputs — traditional ML models, RAG endpoints, and agentic LLM services — into user-facing Streamlit applications.
· Own the last mile from model to product: wire in model and data services, design the user experience, and keep the integrated apps performant and dependable in production.
· Partner with data scientists to turn their work into shipped, supported tools rather than one-off notebooks or prototypes.
· Build and maintain supporting reporting in other BI tools such as Power BI where a full application isn’t warranted.
Business Impact & Collaboration
· Partner with GTM, sales ops, customer success, and initiative owners to identify high-impact opportunities and translate business intent into the right data product.
· Communicate clearly with both technical and non-technical audiences, and stand behind the accuracy of the numbers your apps surface.