Senior Data Architect, TILT

• We are not looking for someone who just writes SQL
• We are looking for someone who understands data deeply, thinks structurally, and can design systems that last
• 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
• If you don’t meet every single requirement in the job description, please don’t be put off from applying. We value potential and a willingness to learn over ticking every box — your unique perspective could be exactly what we’re looking for

Desirable

• We are looking for a Senior Data Architect to own and evolve our end-to-end data platform
• This is a hands-on, high ownership role
• You will be responsible for the architecture, reliability, and scalability of our data warehouse, transformation layer, orchestration, and analytics stack
• You will navigate and improve our existing systems across Snowflake, Dagster, dbt, Postgres, Metabase, and ContextFlow, while defining standards that allow us to scale intelligently
• What You’ll Do 0–3 months:
• 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
• What You’ll Do 3+ months:
• 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