You will own the end-to-end development and deployment of agentic AI use cases, from ideation to production. This involves building modular agent skills, modeling enterprise ontologies, and engineering data pipelines to support complex business workflows.
You will own the full lifecycle of client engagements, from technical discovery and scoping to hands-on implementation of the first sprint. You are responsible for managing a book of business, accurately forecasting revenue, and maintaining strong partner relationships with Snowflake and Databricks.
You will embed with client teams to map complex business processes and translate them into production-grade agentic AI workflows. You are responsible for building the agent logic, managing system integrations, and ensuring the solutions are reliable and maintainable.
The Senior Data Engineer will design, build, and maintain scalable ELT pipelines using Snowflake, dbt, and Apache Airflow. They will also mentor team members, optimize platform performance, and support downstream reporting through modern BI tools.
The role involves building production-grade agentic workflows and LLM-powered systems while managing data pipelines and semantic context layers. You will also be responsible for establishing evaluation practices, observability, and providing technical leadership to clients.
The Principal Solutions Architect will lead technical discovery, design Lakehouse architectures, and manage the Databricks partnership strategy. They will also serve as the lead architect on major client engagements, overseeing technical delivery and team performance.
Lead the technical sales process by conducting discovery and designing architectures to win new deals. Transition from sales to delivery by guiding the technical direction and writing production-grade code for client engagements.
Act as a force multiplier for the founding team by bridging the gap between technical innovation and strategic execution. This includes prototyping internal AI workflows, managing company-wide operating cadences, and optimizing organizational systems to remove bottlenecks.
Lead client engagements by translating complex business questions into structured analytical problems and data products. Develop production-quality SQL models and dashboards while leveraging AI tools to accelerate delivery and improve quality.
