← CarreiraAI
Indeed
Híbrido

Data Engineer (all genders)

Eraneos · Hamburg, München, Düsseldorf, remote

Publicada em 27/08/2026

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What you can expect from usWe’re a high‑energy cloud- and data engineering team looking for like-minded, motivated tech builders. We are driven by ownership and business outcomes.  As the analytics unit within a global management consultancy, we turn data-driven strategies into production systems. Our clients are DAX and Fortune Global 500 leaders who trust us for our expertise across data platforms, big data and cloud engineering. You’ll join an international environment with agile squads and a boutique spirit with room to experiment and grow, paired with above‑market compensation and an attractive benefits package.What you’ll do as part of our cloud- and data engineering team: Take ownership for projects end to end: From scoping to stakeholder alignment and the delivery of measurable outcomesDesign data architectures for batch and streaming workloads, both at small and big data scalesProvision and manage the underlying cloud infrastructure to support those architectures on providers such as AWS, Azure or GCPBuilding Python ETL/ELT pipelines, orchestrated with Airflow, Dagster, or Prefect.Work with data lake technologies and optimize (OLAP) data ware-/lakehousesModel and store data across SQL and NoSQL databases, as well as optimizing performance and costsCreate web APIs with popular Python frameworks (e.g.,FastAPI and Flask) and describe them using standards likeOpenAPI/SwaggerAutomate deployments using Infrastructure-as-Code and container technologies such as Docker, Kubernetesor OpenShiftWho you areA passionate data engineer who loves being at the forefront of modern, AI-driven technology, but also cares about the engineering craftmanship.Relevant hands-on experience with a cloud hyperscaler (preferably AWS,Azure or GCP).Preferably experience building production-grade software.Python development with idiomatic and well-tested code.Skilled in performance tuning and optimization of relational databases (e.g., through indexing,shardingor partitioning).Experienced w
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