About the job
Join Tilt as a Senior Data Architect!
At Tilt, our mission is clear: Make Commerce Alive. We are revolutionizing the traditional commerce landscape, moving away from outdated store website builders and impersonal marketplaces to create engaging, community-driven experiences for a new generation of merchants. In the UK, millions of enthusiastic shoppers, from sneaker collectors to Y2K fashionistas, have embraced Tilt. Our platform has empowered many sellers to achieve over £1M in earnings, with numerous others surpassing the UK median income. And this is just the beginning!
Your Role
We are on the lookout for a Senior Data Architect who will take charge of our comprehensive data platform. This hands-on position demands a high level of ownership. You will be tasked with overseeing the architecture, reliability, and scalability of our data warehouse, transformation layer, orchestration, and analytics stack. Your role will involve enhancing our existing systems using tools such as Snowflake, Dagster, dbt, Postgres, Metabase, and ContextFlow, while establishing standards that facilitate intelligent scaling. We seek someone with a profound understanding of data, who can think structurally and design sustainable systems, rather than just writing SQL.
Your Responsibilities
First 0-3 Months
Gain an in-depth understanding of our current Snowflake warehouse architecture
Audit and refine existing dbt models for clarity, performance, and accuracy
Review and stabilize Dagster orchestration pipelines
Identify data quality issues and implement testing standards
Ensure key dashboards in Metabase are supported by reliable, well-structured datasets
Document the current system architecture and establish architectural standards
After 3+ Months
Redesign and optimize warehouse architecture as necessary
Implement scalable data modeling patterns across various domains
Enhance cost efficiency and performance within Snowflake
Introduce stronger observability, testing, and data lineage practices
Enable AI and product features by structuring high-quality, production-ready datasets
Establish clear version control and deployment workflows for data modifications
Collaborate with product and engineering teams to define data contracts and ownership responsibilities

