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Pegel

Data engineer jobs in Berlin with no German required

26 active roles

Data engineering at Berlin startups is almost always English-first, and the reason is embedded in the hiring profile for the role. Companies that have invested enough in data infrastructure to need a dedicated data engineer are companies that are past the first scrappy phase, have a real analytics function, and have usually assembled a technically international team. The warehouse and orchestration stack these engineers work with, pipelines included, is entirely English-language tooling, and the people they collaborate with (analytics engineers, product analysts, ML engineers) are typically the most internationally recruited part of any startup.

The no-German rate for data engineering roles in the data I collect is among the highest of any technical discipline. This is not to say German never appears on a data engineering job description in Berlin. Some companies want a data engineer who can also communicate directly with German-speaking business stakeholders, and those roles would not pass the no-German filter. But the structural tendency is clear.

What these roles look like in practice: the Berlin data engineering cohort is heavily weighted toward dbt, Airflow or Dagster, a cloud data warehouse (BigQuery most often, Snowflake second, Redshift occasionally), and Python for orchestration glue. Spark appears at companies with larger data volumes. Kafka and event-streaming infrastructure come up at fintechs and companies with high-throughput product analytics. Some listings sit at the boundary between analytics engineering and platform engineering; the person building the semantic layer and the person building the orchestration framework are not always different people. I keep every description verbatim so the team's definition of the role is the one you read.

Pegel refreshes daily. A data engineering role posted overnight will be on this page the following morning.

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Questions

What tools should I expect to see in Berlin data engineering roles?
dbt and a cloud warehouse are nearly universal at companies past early stage. Airflow or Dagster for orchestration. Python as the connective language. The gap between companies is less about which tools and more about whether the stack is maintained and monitored, with real documentation, or whether you are walking into a pile of untouched tech debt. The job description rarely distinguishes clearly, but the stage of the company and the size of the data team are reliable proxies.
Are data engineering roles at Berlin startups typically individual contributors or do they manage analysts?
Mostly individual contributors at startup scale, though the framing varies. A staff or lead data engineer may have informal technical authority over analytics engineers or embed in decisions about tooling and standards without carrying a management title. True management of a data team is rare until a company is well past Series B. If management is part of the scope, the job description will usually say so.
Is data engineering in Berlin growing faster than other data-adjacent roles?
The volume of data engineering postings I see relative to data science and analytics engineering has been rising. The shift toward engineered data products, the rise of the modern data stack, and the growth of AI features in products that need clean training pipelines have all created demand that did not exist a few years ago.
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