About the Team
Behavioral Data & Experimentation Platform is a cross-functional team with end-to-end ownership of digital tracking across all markets globally at HelloFresh - from infrastructure all the way to curated data products and the hypotheses validation mechanisms (eg. AB testing, quasi-experiments). The squad brings together software engineers, data engineers, and data scientists to build the tracking, event, and statistical infrastructure that every tech team relies on to drive product decisions. Our work enables customer experience improvements and business growth.
About the Role: What's in the Box
As our Senior Data Engineer, you'll play a crucial part in extending and operating the platform behind our behavioral data - from event & experiment pipelines to Databricks Unity Catalog and semantic layer integrations, to the systems and standards that let other teams validate hypotheses on top of our platform. This isn't a pure data pipeline-building role: you'll reason about system design, schema governance and develop a technology platform as much as data engineering. Your deep understanding of behavioral tracking and data platform systems enables you to identify pain points across Behavioral Data Collection and Experimentation, and to design solutions that improve how we work at scale.
What You'll Do: The Recipe
At HelloFresh we are moving away from a model where software developers just execute tickets toward one where product engineers are trusted to own customer problems. A Product Engineer takes a problem, forms a point of view, validates it with customers and data, and ships it using AI as a force multiplier.
- Become an integral part of a high-impact data platform team, covering behavioral and experimentation data products.
- Design, build, and operate the platform components behind our behavioral data and experimentation infrastructure (Snowplow, Statsig, Databricks, Unity Catalog).
- Leverage Generative AI to improve and add new capabilities to the data platform.
- Build high-quality, well-modeled data pipelines for analytical purposes, enabling reliable reporting, experimentation analysis, and self-serve insight at scale across HelloFresh.
- Own reliability and scalability of data platform components - schema governance, event standards, lineage, and data quality checks.
- Extend and evolve our semantic layer, partnering with domain teams who federate metric definitions on top of it.
- Work with state-of-the-art technologies like Snowplow, Statsig, AWS, Airflow, Databricks Unity Catalog and Metric Views.
- Collaborate with product managers, product analysts and data scientists to create reusable, well-governed data products and to ensure the platform supports fast, trustworthy experiment readouts.
- Contribute to a collaborative and knowledge-sharing environment, providing technical guidance and coaching to team members and stakeholders.
- Work beyond your specialization when the problem demands it. Your specialization is your anchor, not your boundary.
- Operate what you build. You instrument, monitor, and improve your systems in production. Shipping is the beginning of the learning cycle, not the end.
What You'll Bring: The Ingredients
- Hands-on experience working with AI tooling (e.g., Claude Code, Cursor, Copilot) beyond casual experimentation. You use AI agents every day. You have a practical sense of how the context you provide to AI tools shapes output quality, and how to set boundaries on AI-generated work.
- Proven track record of scientific or engineering work (Bachelor's, Master's, or PhD) in computer science, software engineering or related field.
- 5+ years of work experience as a Data Engineer, including delivery of production data. 2+ years on data platform systems - eg. semantic layers, catalogs, schema/governance systems.
- Fluency in Python and dbt, and building large scale high quality data pipelines on a modern cloud stack.
- Technical proficiency in SQL for retrieving, manipulating, and analyzing large datasets.
- Experience with software development practices and tools such as Git, CI/CD, and AWS.
- Expertise in leveraging Generative AI tools (eg. Claude, Copilot) to support development, experimentation, and documentation.
- Exceptional communication capabilities to interact effectively with product managers, product analysts and business stakeholders.
- You take full ownership. You have a bias to ship. You finish the last twenty percent.
- You have product sense and are opinionated about what should be built and why, and you can back that opinion with data and user evidence.