Data Engineer
SKILLS
FULL DESCRIPTION
Data Engineer
We’re looking for a hands-on, detail-loving Data Engineer to help us level up our data foundations and set us up for bigger, bolder analytics. This is our second data hire, which means you won’t just be maintaining something that already exists, you’ll be helping define how it should work end-to-end.
Key Responsibilities
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.
Key Responsibilities:
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, including: Adalyser, Meta Ads, Google Ads, HubSpot, Aircall, Performance tracking data, Product imagery + metadata from bespoke internal platforms, Maintain consistency and quality across the ecosystem as new sources come online.
Build automated checks to monitor accuracy, completeness, and freshness. Run regular audits and troubleshoot issues quickly and calmly. Create clear ownership and definitions for key data sets.
Identify opportunities to streamline pipelines, improve performance, and reduce cost. Automate repetitive workflows to free teams up for higher-value analysis. Improve reliability and speed of reporting inputs.
Work closely with teams across Growth, Finance, Ops, and Product to understand KPIs and reporting needs. Translate those needs into smart, scalable data solutions. Communicate clearly with both technical and non-technical folks, no jargon fog.
Document architecture, pipelines, models, and workflows so everything is clear and easy to pick up. Contribute to data standards and governance as we build out the function. Share knowledge openly and help shape how data engineering is done at Vintage.com and Vintage Cash Cow. .
Skills, Knowledge and Expertise
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.