Senior Analytics Engineer

at  Trustpilot

ABOUT US

Trustpilot began in 2007 with a simple yet powerful idea that is more relevant today than ever — to be the universal symbol of trust, bringing consumers and businesses together through reviews. Trustpilot is open, independent, and impartial — we help consumers make the right choices and businesses to build trust, grow and improve.
Today, we have more than 300 million reviews and 67 million monthly active users across the globe, with 127 billion annual Trustpilot brand impressions, and the numbers keep growing. We have more than 900 employees and we’re headquartered in Copenhagen, with operations in Amsterdam, Denver, Edinburgh, Hamburg, London, Melbourne, Milan and New York.
We’re driven by connection. It’s at the heart of what we do. Our culture keeps things fresh –– it’s built on the relationships we create. We talk, we laugh, we collaborate and we respect each other. We work across borders and cultures to be the universal symbol of trust in an ever-changing world. With vibrant office locations worldwide and over 50 nationalities, we’re proud to be an equal opportunity workplace with diverse perspectives and ideas.
Our purpose to help people and businesses help each other is a tall order, but we keep it real. We’re a great bunch of humans, doing awesome stuff, without fuss or pretense. A successful Trustpilot future is driven by you –– we give you the autonomy to shape a career you can be proud of. If you’re ready to grow, let’s go.
Join us at the heart of trust

You will be maintaining our data warehouse and business intelligence tool, while supporting consumers of data across the organisation!

• Designing, building, maintaining and monitoring data pipelines and models, leveraging GCP, Airflow, and other modern data tools.
• Rolling out our new Data Cataloguing tool to help expose data across the business.
• Looking after our business intelligence tool, Looker.
• Assisting stakeholders with building their own dashboards, reports and data solutions.
• Advocate for data as a product to ensure data is documented, reviewed, and accurate.
• Optimising our data warehouse and development methods, reducing complexity and costs where possible.