Data Engineer

Department: Data

Employment Type: Full Time

Location: Trimble Offices, Morley

Description

• Get familiar with our current data setup and BI stack (Snowflake, dbt, FiveTran, Sigma, SQL, and friends).
• Meet your key partners across Growth/Marketing, Finance, Operations, and Product to understand the metrics that matter most.
• Explore our core data sources (Adalyser, Meta Ads, Google Ads, HubSpot, Aircall, and internal platforms) and how they flow today.
• Spot early wins: where pipelines can be simplified, data quality boosted, or reporting made faster and smarter.
  
  

• Build and optimise reliable, scalable ELT/ETL pipelines into Snowflake.
• Create clean, reusable models that make downstream analytics simple and consistent.
• Put proactive monitoring and validation in place so teams can trust what they see.
• Reduce manual work across reporting and data movement through automation.

• Help define and evolve our data architecture as we scale into new markets.
• Champion best practice: documentation, governance, naming conventions, testing, and performance.
• Partner closely with stakeholders so data engineering solves real commercial problems (not just technical ones).
• Keep one eye on what’s next: smarter tooling, AI-assisted analytics, and ways to make our stack even more self-serve.

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.

• 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.

• 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.