Senior Data Product Engineer, Intelligent Platforms (all genders)

HelloFresh is a global digital-native FMCG company and the world's leading mealkit provider transforming the traditional food supply chain through a data-driven, on-demand model. The HelloFresh Group includes multiple brands offering meal kits, ready-to-eat meals, meat and seafood subscriptions, and pet nutrition. The role is based in Warsaw within HelloTech, HelloFresh’s global technology backbone of over 1000 people, building digital products that power the end-to-end food experience across markets.

• Hands-on experience with AI tooling such as Claude Code, Cursor, and Copilot beyond casual use.
• Proven scientific or engineering background with Bachelor's, Master's, or PhD in computer science, software engineering, or related field.
• Over 5 years of experience as a Data Engineer with production data delivery and at least 2 years on data platform systems including semantic layers and schema/governance systems.
• Fluency in Python and dbt, building large scale, high quality data pipelines on modern cloud stacks.
• Technical proficiency in SQL for data retrieval, manipulation, and analysis.
• Experience with software development tools and practices such as Git, CI/CD, and AWS.
• Expertise in leveraging Generative AI tools for development, experimentation, and documentation.
• Exceptional communication skills for effective interaction with product managers, analysts, and business stakeholders.
• Strong ownership mindset with a bias to ship and complete projects.
• Product sense with ability to justify features based on data and user evidence.

• Design, build, and operate platform components behind behavioral data and experimentation infrastructure such as Snowplow, Statsig, Databricks Unity Catalog.
• Leverage Generative AI tools to improve and add new capabilities to the data platform.
• Build high-quality, well-modeled data pipelines for reliable reporting, experimentation analysis, and insight at scale.
• Own reliability and scalability of data platform components including schema governance, event standards, lineage, and data quality checks.
• Extend and evolve the semantic layer, working with domain teams federating metric definitions.
• Collaborate with product managers, analysts, and data scientists to ensure fast, trustworthy experiment readouts.
• Contribute technical guidance and coaching to team members and stakeholders.
• Operate the systems you build by instrumenting, monitoring, and improving them in production.
• Take end-to-end ownership from problem definition to system delivery and scalability improvements.