Build and maintain a modern, scalable data platform that supports growth and decision-making.
Ensure data is accurate, consistent, and trusted across the business.
Improve speed, reliability, and automation of data pipelines and reporting workflows.
Enable high-quality self-serve analytics by delivering well-modelled, well-documented data sets.
Support digital performance and CRM insight through strong marketing data foundations.
Design, implement, and maintain robust data pipelines across multiple systems.
Ensure smooth, well-governed flow of data from source → warehouse → BI layers.
Support end-to-end warehouse design and modelling as our stack grows.
Integrate and manage a wide range of data sources within Snowflake.
Build automated checks to monitor accuracy, completeness, and freshness.
Identify opportunities to streamline pipelines, improve performance, and reduce cost.
Work closely with teams across Growth, Finance, Ops, and Product to understand KPIs and reporting needs.
Document architecture, pipelines, models, and workflows so everything is clear and easy to pick up.

Strong Snowflake experience: loading, querying, optimising, and building views/stored procedures.
Solid SQL skills: confident writing complex queries over large datasets.
Hands-on pipeline experience using tools like dbt, FiveTran, Airflow, Coalesce, HighTouch, Rudderstack, Snowplow, or similar.
Data warehousing know-how and a clear view of what “good” looks like for scalable architecture.
Analytical, detail-focused mindset: you care about quality, reliability, and root-cause fixes.
Great communication: able to explain technical concepts in a simple, useful way.
Comfortable working in a small, high-impact team where you’ll shape the roadmap.
Nice to have: Experience working with HubSpot data (ETL into a warehouse, understanding the schema, reporting context).
Digital marketing analytics background: ads platforms, attribution, funnel performance, campaign measurement.
Familiarity with CRMs/marketing automation tools (HubSpot, Marketo, Salesforce, etc.).
Python or R for automation, data wrangling, or pipeline support.
Understanding of A/B testing or experimentation frameworks.
Exposure to modern data governance/catalogue tooling.

Data Engineer

Key skills