As an Analytics Engineer in Marketing, you’ll design, build and maintain the business critical marketing data pipelines that teams rely on to optimize marketing performance and decision-making. Specifically, you will develop robust, future-proof pipelines that ingest, process, and monitor data from diverse external sources to ensure our teams have access to high-integrity data. In this role, you’ll have the opportunity to build a deep understanding of the marketing domain while proactively identifying data-related process optimization opportunities.
As an Analytics Engineer in Marketing, you’ll design, build, and operate the marketing data pipelines that teams rely on to optimize marketing performance and decision-making.
You’ll partner closely with:
- Marketing Operations Intelligence and Marketing Modelling Intelligence teams, who use this data daily for performance reporting and model-driven optimisation.
- Other Analytics Engineers in Marketing, with shared ownership of the marketing data domain
- A broader 40+ person Analytics Engineering Guild across Vinted, where you’ll benefit from established patterns, code reviews, reusable dbt and python components, and regular knowledge sharing.
This is an end-to-end role across BigQuery, dbt, Airflow: from raw ingestion and transformations through to trusted, well-documented datasets and domain-ready reporting.
You will report to the Manager of Data Science & Analytics, Marketing Analytics Engineering.
In this position, you’ll
- Design and build scalable and reliable marketing data models in dbt/BigQuery/Looker that turn marketing operations and modelling data into Decision-ready datasets used for reporting, analysis and optimization.
- Implement data quality checks and controls (tests, regressions, anomaly detection) to increase reliability and consistency of reporting.
- Own production reliability by investigating incidents end-to-end, performing root-cause analysis, and shipping fixes that prevent repeat failures.
- Translate Marketing needs into clear technical delivery by gathering requirements in plain language, aligning definitions and turning them into documented models and metrics.
- Drive adoption and correct usage by writing practical documentation, enabling self-serve analysis.
