Data Engineer

Role overview

Join Vintage.com as a Data Engineer to build and maintain the data foundations that power impactful analytics. You’ll design robust data pipelines, improve data quality, and translate stakeholders’ metric needs into scalable warehouse models and data platform capabilities.

Key missions

• Design and build data pipelines and shape data warehouse models.
• Ensure data quality (accuracy, consistency, and trust) to support business decisions.
• Collaborate with stakeholders across teams to understand metrics and implement scalable data solutions.
• Maintain and optimize a modern data platform, focusing on reliability and data governance.

Responsibilities

• Build and maintain a scalable analytics data platform.
• Develop and optimize Snowflake artifacts (loading, querying, views, stored procedures).
• Support analytics and decision-making by providing trustworthy data outputs.
• Troubleshoot data issues with a root-cause mindset.
• Apply domain knowledge from digital marketing analytics (metrics, attribution, funnels, campaign measurement).

Requirements

• Data warehousing fundamentals and architectural judgment for scalable systems.
• Strong Snowflake experience: loading, querying, optimization, and building views/stored procedures.
• Strong SQL skills for complex queries over large datasets.
• Clear communication: explain technical concepts simply and effectively.
• Hands-on pipeline experience with tools such as dbt, FiveTran, Airflow, and related equivalents.
• Analytical, detail-focused mindset with emphasis on quality and reliability.
• Digital marketing analytics background (ads platforms, attribution, funnel performance, campaign measurement).
• Experience working with HubSpot data (ETL into a warehouse, schema understanding, reporting context).
• Familiarity with CRM/marketing automation tools (e.g., HubSpot, Marketo, Salesforce).

Nice to have

• Python or R for automation, data wrangling, or pipeline support.
• Understanding of A/B testing and experimentation frameworks.
• Exposure to data governance/catalog tooling.
• Experience with additional pipeline/collection tools (e.g., Coalesce, HighTouch, Rudderstack, Snowplow) or similar.