Berlin, Berlin, Germany
Live, first seen by us 62 days agoAt 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.
This is a hands-on engineering role at the core of our platform.
Build and run streaming and messaging systems such as Kafka, Kafka Connect, Flink, and SQS
Build Terraform modules that let teams provision operational data stores within minutes
Raise data quality for operational data, including data quality checks on Kafka
Build AI-powered tooling so teams can operate their own data infrastructure
Participate in on-call rotations and automate toil for systems you own
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.
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.
Build and run our streaming and messaging platform.
Build and maintain the Terraform modules and tooling that let engineering teams use the right database for their use case within minutes
Raise the bar on data quality for operational data, including data quality checks on Kafka
Develop tooling — including AI and agent-based capabilities — that lets teams operate and maintain their own data infrastructure
Participate in on-call rotations for the systems you own, and invest in automation
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Day 62
Up longer than most. Similar jobs usually come down between day 6 and day 54.
At HelloFresh, jobs usually come down by about day 49.
From 11,069 similar postings that came down, and 33 of HelloFresh’s. Coming down is not always a hire.
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- 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.
- 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.
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.
- 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.
- 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.
Nice to have - Experience with Flink, stream processing, Kafka Connect. - Experience building self-service developer platforms.
- 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.
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New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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.
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#Data
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Berlin, Berlin, Germany
Flexibility & Work-Life - Balance your life with our flexible hybrid model. - Recharge with sabbatical leave options in addition to standard time off.
- 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
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