Senior Data Platform Engineer, Intelligent Platforms (all genders)
HelloFresh is hiring a Senior Data Platform Engineer, Intelligent Platforms (all genders) in Berlin, Germany. Level rates it ; you can apply on HelloFresh.
Level's read on this job
- On Level's scale, this role is Works on AI ●●●○: the daily work is on AI products, without building the model.
- Tools and skills the posting cites include Kafka, Kafka Connect and Flink.
- Level classifies it as a senior role.
- Level lists 384 other open roles at HelloFresh.
AI in this role
Senior Data Platform Engineer building real-time streaming platforms, data infrastructure, and AI-powered self-service tooling at HelloFresh.
About the role
At HelloFresh, data is at the heart of everything we do — from the moment a customer places an order to the way we forecast demand, optimize our supply chain, and personalize experiences for millions of people. Making that data fast, reliable, and available is what our Data Platform makes possible.
We are looking for a Senior Data Platform Engineer to join our Operational Data Platform team — the squad that ensures operational data is created, delivered, and shared downstream reliably, cost-efficiently, and with the quality guarantees that everything downstream depends on — so the analytics layer receives quality data, ready to be turned into governed assets. You will build the streaming, messaging, and self-service provisioning systems that hundreds of backend and data engineers rely on every day.
This is a hands-on engineering role at the core of our platform. You'll own critical infrastructure, drive efficiency, and help define the "golden path" that makes it effortless for engineers across the company to store and stream their data — with governance and cost control built in from the start.
What you'll do
- Build and run our streaming and messaging platform. Own the systems that move data in real time and at scale — Kafka, Kafka Connect, Flink, and SQS — and keep them fast, resilient, and easy to build on.
- Drive efficiency. Identify and act on opportunities to reduce the cost and complexity of the platform, and make efficiency a default rather than an afterthought.
- Own the reliability of critical data paths. Keep the systems at the heart of the platform highly available, and continuously improve their resilience, observability, and operational maturity.
- Enable self-service for operational data stores. Build and maintain the Terraform modules and tooling that let engineering teams use the right database for their use case within minutes, with governance and cost guardrails out of the box.
- Build AI-powered self-service. Develop tooling — including AI and agent-based capabilities — that lets teams operate and maintain their own data infrastructure, so the platform runs without us in the loop.
- Raise the bar on data quality for operational data, including data quality checks on Kafka, so downstream analytics can trust the data from the moment it's created.
- Collaborate across the platform. Work closely with the Analytical Data team so high-quality operational data flows seamlessly into the analytics platform through clean, well-defined handover points.
- Operate what you build. Participate in on-call rotations for the systems you own, and invest in automation so the platform runs without a human in the loop.
What you'll bring
- Hands-on experience as a data, software, or platform engineer, with a track record of building and operating production data infrastructure at scale.
- Strong software engineering fundamentals and proficiency in a backend language (e.g., Go, Java, Scala, or Python).
- Deep experience with event streaming and messaging systems — Kafka in particular — including their operational characteristics in production.
- A solid understanding of databases (relational and NoSQL — e.g. PostgreSQL, MongoDB, DynamoDB) and the trade-offs between them.
- Experience building infrastructure on AWS with infrastructure-as-code (Terraform), containers, and Kubernetes.
- A strong ownership mindset — you care about reliability, efficiency, and the developer experience of the teams you serve.
- Comfort with on-call responsibilities and a bias toward automating away toil.
- Excellent communication skills and the ability to collaborate across engineering teams to design seamless, composable services.
Nice to have
- Experience with Flink, stream processing, Kafka Connect.
- Experience building self-service developer platforms.
- Familiarity with data lake architectures.
What we offer
Compensation & Financial Wellbeing
- You’ll receive a competitive salary package, including a HelloFresh-subsidized pension scheme.
- Enjoy savings on your weekly HelloFresh box.
- Save with discount schemes with corporate benefits and Vorteile.
Health & Wellbeing
- Support your mental wellbeing with access to platforms like Headspace and Spill.
- Stay active with 24/7 office gym access, a discounted Urban Sports Club membership, and other wellbeing resources.
Flexibility & Work-Life
- Balance your life with our flexible hybrid model.
- Recharge with sabbatical leave options in addition to standard time off.
- Get to work with ease through sustainable travel perks and transportation benefits.
Career & Development
- Embrace "Learning Never Stops" with internal training, the HelloFresh Academy, and a dedicated budget for German language courses.
Culture & Environment
- Enjoy tasty in-office meal deals and fresh food options.
- We support working families with parent-friendly policies, an in-office kids corner, and an on-site babysitter.
#Data
How we rate this
Senior Data Platform Engineer, Intelligent Platforms (all genders) at HelloFresh rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where streaming was part of your work. What did you do?
- Tell me about a project where distributed systems was part of your work. What did you do?
- Tell me about a project where self service tooling was part of your work. What did you do?
- Walk me through how you've used Kafka in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Data Engineering, Streaming, Distributed Systems, Self Service Tooling, and Kafka. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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