Staff Data Engineer, Data Alliance (f/m/x)

at  Alumni Network Job Board

STAFF DATA ENGINEER, DATA ALLIANCEBERLINTHE BEHAVIOURAL DATA PRODUCTS SQUAD

Our Behavioral Data Products squad has end-to-end ownership of behavioral data tracking across all markets globally at HelloFresh, from infrastructure all the way to the curated data products. As our Staff Data Engineer, you will play an integral part in ensuring data quality, governance and observability by modeling high-value data products and building efficient pipelines to power these data assets.

THE ROLE

If you are an expert analyst and a self-starter, with the product-sense to create highly effective data products to power our global business, this could be the role for you. Your deep understanding of behavioral tracking data, high-volume data pipelines processing over 1TB of fresh data per day, and how this data should be consumed efficiently, will ensure your impact across the breadth of HelloTech, proactively identifying needs and pain points across Data Infrastructure, Platform, Engineering and Analytics, and defining solutions to constantly improve the way we work.
So while you are an individual contributor, you are a key owner of best practices across our teams. Therefore, if you possess the passion and drive to excel in this role, we encourage you to share your story with us, regardless of whether you check all the boxes. We’re eager to learn more about you and how you can enrich our team.

WHAT YOU’LL DO

• Collaborate with and advise Engineering teams in designing technical solutions, ensuring coherence with our target data architecture.
• Build data models and pipelines for our behavioral data collected across all our markets, brands and platforms, powering the models, analyses and dashboards by our key stakeholders: Global AI, Product Analytics, Marketing Data Product & Analytics, and business teams.
• Demonstrate an in-depth understanding of HelloFresh’s core product and architecture, serving as an ambassador for data solutions and best practices while providing support and mentorship to colleagues.
• Address software and data challenges with visible risks, roadblocks, and constraints, such as legacy dependencies, data quality issues, compliance concerns, or limitations in architecture or technology.
• Advocate for (Data) Operational Excellence, assisting teams and Engineering Leadership in identifying and prioritizing investments that enhance data quality and the robustness of data pipelines.