Join the Frisbii Team!

We are looking for a motivated and skilled Data & AI Engineer with experience in Data and AI Engineering.

This is a hands-on role for a team with a history of data engineering, but more recently owning key elements of our agentic AI automation roadmap using technologies such as Snowflake, DBT, and AI tools on AWS Bedrock Agentcore.

You will collaborate closely with experienced engineers, product managers, designers, and other stakeholders to apply sound engineering principles, write high-quality code, and build reliable solutions that solve real business problems at scale.

Job requirements

Your Responsibilities

• Data Platform & Pipeline Management
• Develop and Enhance our DataLake (AWS, Snowflake).
• Get involved in all steps of the data lifecycle, including ingestion, processing, and serving.
• Design and implement data models and pipelines on various technologies (AWS Glue, dbt).
• Take responsibility for all the dimensions of the infrastructure, also including monitoring, testing and updating to new technologies.
• Agentic AI
• Develop and enhance our MCP and AI Agentic technologies.
• Design AI agents using AWS Bedrock Agentcore.
• Collaboration & Cross-Team Initiatives
• Work closely with the product team to refine our product offering and drive innovation.
• DevOPS Integration & Best Practices
• Partner with the DevOps teams to bring your solutions into production (Terraform, DataOps, CI/CD).
• Implement best development practices, including testing, live monitoring, continuous integration, code deployment, and end-to-end automation.
  
  

What We’re Looking For

• Experience in Data Engineering technologies such as Snowflake, DBT, Python.
• Experience or skills to adapt to the needs of Agentic AI technologies.
• Experience of working with source control systems, application lifecycle management systems and continuous build systems is highly desired.
• Experience of agile practices such as Scrum/Kanban.
• Experience of AI tooling such as Cursor, Github Co-pilot.
• Experience of working on multiple projects in parallel.
• Been involved in the full product lifecycle from concept to release, preferably working for a software product vendor.
• Implementing and adhering to processes and procedures throughout the software development lifecycle.
• Proficiency in English.