In YouGov Shopper, we believe that constant innovation is the key to improve the pertinence and quality of the data that we receive from our consumer panel members and app users.   

Our team’s goal is to reduce the associated workload by increasing automation. We achieve this by combining panelist data with a large variety of additional data sources, using creative data science and an increasing usage of LLMs.  

  
As a Data Engineer, you will join our cross-functional team of Machine Learning Engineers and Data Scientists to develop new components for our text-matching framework.  

You will support data scientists with their day-to-day work, supporting a seamless research-to-production workflow regarding data and code, working closely with our SREs and software engineers to integrate our DS/ML components with our productive data processing pipelines on AWS. 

This role follows a hybrid work model, with two days per week in the office; the collaboration will be based on an employment contract.

Here’s a breakdown of what you’ll do:

• Design, build, and maintain ETL/ELT pipelines and data workflows across batch and streaming systems. 

• Develop and optimize data models for analytics, reporting, and machine learning use cases. 

• Ensure data quality, consistency, and governance across multiple data sources. 

• Work on efficient CI/CD pipelines to continuously deploy our new features and models.  

• Set up and refine ML and data pipelines. Define model quality and set up a monitoring and alerting system.  

• Work closely with software engineers and SREs on the integration of our components.  

• Present your achievements to a broader audience, i.e. during agile ceremonies, internal knowledge-sharing events or conferences. 

• Contribute to the evolution of our data platform, including architectural improvements and tool selection. 

These are the essentials for having an interview:

• 4+ years of experience as a Data Engineer or in a similar role. 

• Hands-on experience with cloud platforms for data engineering workloads. 

• Experience collaborating with data scientists and supporting ML pipelines. 

• Familiarity with DevOps practices (CI/CD, infrastructure as code, containerization with Docker/Kubernetes).  

• Working experience with most of our tech-stack.  

• Clearly and confidently in English within a business and technical context. 

• You think beyond the obvious, approaching problems with creativity and innovative solutions. 

• You can balance technical complexity with business value, estimating effort versus impact effectively. 

• You’re self-driven, eager to learn, and proactive in supporting teammates—whether it’s debugging issues, setting up tools, or sharing best practices.

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 ....nice to have skills:

• Good knowledge about using LLMs for research and productive use.  

• Experience with streaming platforms (Kafka, Kinesis, or Pub/Sub). 

• Experience with working in an agile environment (Scrum, JIRA). 

• Good knowledge of machine learning algorithms, preferably in NLP and text recognition.