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.
Run regular audits and troubleshoot issues quickly and calmly.
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.
Experience working with HubSpot data (ETL into a warehouse, understanding the schema, reporting context) is nice to have.
Digital marketing analytics background: ads platforms, attribution, funnel performance, campaign measurement is nice to have.
Familiarity with CRMs/marketing automation tools (HubSpot, Marketo, Salesforce, etc.) is nice to have.
Python or R for automation, data wrangling, or pipeline support is nice to have.
Understanding of A/B testing or experimentation frameworks is nice to have.
Exposure to modern data governance/catalogue tooling is nice to have.

Data Engineer

Key skills