Deeply understand our current Snowflake warehouse architecture
Audit and improve existing dbt models for clarity, performance, and correctness
Review and stabilise Dagster orchestration pipelines
Identify data quality gaps and implement testing standards
Ensure key dashboards in Metabase are built on reliable, well-modelled datasets
Document the current system and define architectural standards
Redesign and optimise warehouse architecture where needed
Implement scalable data modelling patterns across domains
Improve cost efficiency and performance in Snowflake
Introduce stronger observability, testing, and lineage practices
Enable AI and product features by structuring high-quality, production-ready datasets
Establish clear version control and deployment workflows for data changes
Partner with product and engineering to define data contracts and ownership

Strong technical background in Computer Science, Engineering, Mathematics or similar. Equivalent practical experience is equally valued
Deeply comfortable navigating modern data stacks, especially Snowflake, dbt, and orchestration tools
Rare combination of analytical rigour and pragmatic execution
You genuinely understand data modelling, not just SQL queries
Highly autonomous. You do not need hand-holding or micromanagement
First principles thinker who questions assumptions and improves systems
Comfortable operating in ambiguity and building structure where none exists
Clear communicator who can explain complex data decisions to non-technical stakeholders
You care about clean systems, reproducibility, and long-term maintainability
Nice to have: Experience with Dagster in production, Familiarity with Postgres operational databases, Experience working with Metabase or similar BI tools, Exposure to AI workflows or tools like ContextFlow, Strong Git habits and version control discipline, Understanding of software development lifecycle and engineering best practices, Experience in fast-moving startup environments

Tilt

Open Positions (1)