Stuart is a leading tech-enabled logistics platform that transforms on-demand delivery across sectors like food, grocery, and retail. Operating in over 130 cities across Europe, Stuart connects businesses with a network of independent couriers, providing access to fast, flexible, and efficient deliveries.
Our Mission 🚀
We are an impact-driven company that aims to build the future of logistics for a more sustainable world: shared, efficient and reliable. We are committed to creating a new standard for urban deliveries that meet today’s environmental and social challenges while offering a premium delivery experience blending speed, flexibility and convenience.
Stuart is a highly diverse and inclusive company of 280+ employees from different nationalities and backgrounds working across France 🇫🇷, Italy 🇮🇹, Poland 🇵🇱, Spain 🇪🇸 and the UK. 🇬🇧
It’s the right moment and the right place for us to make an impact on millions of people, as home delivery services hit a record high. And guess what? You can help us fulfil our vision 🙌
Let's talk about the Analytics Engineering Team ⭐️
Our vision is to enable a data-driven decision-making culture at Stuart. We want to reduce the time to insight into the company by ensuring the availability, reliability, and accessibility of the data across all teams and countries at Stuart.
As an Analytics Engineer, you will get to know everything about Stuart’s key products and projects from a data standpoint. You will build high quality data pipelines and models, by applying software engineering best practices to analytics (powered by dbt). We serve all team's analytical needs by integrating new data points in our data lake and building gold-standard models in our data warehouse as well as testing and documenting them. Next to that you’ll play a key part in our mission to create a self-serve data culture, empowering everyone in Stuart to answer their own questions by using tools that match their needs.
What will I be doing? 🤔
- Build data pipelines and model data in our data warehouse, to allow anyone across the business to interact with data in the level of detail they need
- Work together with your peers on start-to-end data projects
- Collaborate with our analysts to build scalable reporting. Foster data discovery and strengthen data knowledge across the company by writing comprehensive documentation and metrics definitions
- Maintain, together with the rest of the Analytics Engineering team, the core datasets that are used as a starting point for any analysis in the company
What do we need from you? 😎
- 2+ year of experience working with dbt, SQL and data warehousing
- 2+ years of experience working with Python and its relevant data analysis modules (pandas, numpy, matplotlib, ...etc.)
- 2+ years of experience with reporting and data visualization tools such as Tableau, Looker, etc
- Knowledge of Git and some experience coding as part of a team
- A sense of responsibility and a drive to help Stuart build a self-serve data cultureA natural affinity for structure and automation
- Fluency in English
Our Data stack is made up of AWS S3, Redshift, Airbyte, dbt, Airflow, Castor, Tableau and Superset.
The stuff you want to know 😉
At Stuart, we believe that employees today want to evolve in collaborative, high-growth environments where they can demonstrate their abilities and thrive both professionally and personally. We are convinced that employees need to find alignment between their inner values and their company’s culture and mission to unlock their full potential. We work to create a culture of empowerment, continuous learning and growth where everyone can bring expertise, own projects and easily measure their impact 🙌
Stuart is proud to be an equal opportunity workplace dedicated to promoting diversity. We don’t discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status or disability status 💙
Please note: Our Talent Acquisition Team is international coming from across the world 🌍 We kindly ask you to please submit your CV and application in English so that it can be reviewed correctly (unless the job posting is in a language other than English). Thank you 🤗
