Company Description
Flink is the leading quick commerce player in Europe. We are on a mission to give people back time: You can order your groceries through our app and we deliver to your doorstep in minutes. We carry 2,500 products with a focus on the leading brands in each category. We aim for sustainability by making deliveries on electric bikes and utilizing recyclable packaging.
Flink launched in February 2021 and already operates in more than 90 cities in Germany, and the Netherlands. The business generated more than EUR 500m in revenues in 2023 and is operationally profitable. Flink is backed by the world’s most renowned tech investors, including Prosus, Mubadala, and Bond, and strategic investors like Doordash and REWE. We take great pride in being an inclusive employer that offers equal opportunities, and we celebrate the diversity and multiculturalism of our team. We’re growing fast and always pushing ourselves to do better - we play for #1.
Job Description
Are you passionate about topics of data, data quality and building the foundation for impactful business decisions? Do you want to apply your academic knowledge to solve real-world challenges at one of Europe’s fastest-growing companies? Join Flink’s analytics community and work on building and improving our data processes.
As an Analytics Engineering Working Student (m/f/d), you will not just be running queries; you will work alongside experienced engineers and analysts to build scalable processes around one of our key tools used for tracking and experimentation enabling better quality, observability and collaboration.

Among your responsibilities are

• Developing, testing, and documenting robust and scalable workflows for defining tracking concepts, audiences and flags programmatically.
• Ensuring data integrity and quality by implementing data validation and testing protocols.
• Collaborating with data analysts, data engineers and developers to ensure that the solution is complete, robust and scalable.
• Contributing to the continuous improvement of our data infrastructure and engineering best practices.

QUALIFICATIONS

• Currently enrolled in a Bachelor’s or Master’s degree program in Computer Science, information technology, Business Informatics, Engineering, Mathematics, or a related quantitative field.
• A strong understanding of SQL and the ability to write clean, efficient queries.
• Comfortable with objected oriented coding, preferably in Python.
• A genuine curiosity and passion for data integrity and quality.
• Strong communication skills in English and the ability to work effectively in a collaborative team environment.
• Nice to have: First exposure to data warehousing concepts (e.g., BigQuery), data modeling techniques, or data transformation tools like dbt.

• Developing, testing, and documenting robust and scalable workflows for defining tracking concepts, audiences and flags programmatically.
• Ensuring data integrity and quality by implementing data validation and testing protocols.
• Collaborating with data analysts, data engineers and developers to ensure that the solution is complete, robust and scalable.
• Contributing to the continuous improvement of our data infrastructure and engineering best practices