Senior Analytics Engineer - Quick Commerce

As the world’s pioneering local delivery platform, our mission is to deliver an amazing experience, fast, easy, and to your door. We operate in around 65 countries worldwide, powered by tech, designed by people. As one of Europe’s largest tech platforms, headquartered in Berlin, Germany, Delivery Hero has been listed on the Frankfurt Stock Exchange since 2017 and is part of the MDAX stock market index. We push hard, learn quickly and stay human along the way. It’s this wonderful mix of high performance and real community that makes Delivery Hero a place where ambition and belonging grow side by side. If you’re curious, collaborative and ready to dive deep into meaningful work, you’ll fit right in.

Deep analysis. Smarter systems. Real users. As a Senior Analytics Engineer, you'll design and maintain the data pipelines and models that power decision-making across Quick Commerce. You'll work closely with regional, business, product, data science, and analytics teams worldwide to build data solutions that drive our business and fuel new innovations.

You'll join our Analytics Engineering function within Data Foundations, working on the data model setup that powers Quick Commerce decision-making in Google Cloud, built on Apache Spark and Airflow. You'll simplify complex systems, resolve friction points, and refactor for better performance and scalability. Alongside this, you'll help shape and promote strong coding practices, and contribute to how we standardize processes across data foundations, business intelligence, data science, and analytics.

Be part of redefining how customers experience quick commerce. You'll help build technology that scales our non-food offerings, reaching new market segments and driving revenue growth. By innovating within our Quick Commerce Team, you'll make Delivery Hero the go-to platform for a broad range of products, helping us grow faster and deliver more value to customers around the world.

• Design and implement data pipelines, data models, and analytics solutions used across DataMesh

• Partner with cross-functional teams to define data requirements and build the data models that support them

• Build and maintain ETL processes that keep data quality and integrity high while minimizing latency and processing time

• Work with application and analytics teams to turn problem…