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Senior Databricks Platform Architekt

Rhi Magnesita · Produktion

Zusammenfassung

Der Senior Databricks Platform Architekt übernimmt die technische Verantwortung für die Weiterentwicklung der Databricks-Plattform, führt neue Funktionen ein und stellt deren Sicherheit, Skalierbarkeit, Kosteneffizienz und Produktionsreife sicher. Diese Rolle beinhaltet praktische Arbeit, Architekturentscheidungen und Fehlerbehebung in Zusammenarbeit mit verschiedenen Teams und Partnern.

Eckdaten

Status
Verfügbar· vor 29 Tagen
Standort
Wien
Level
Senior
Geforderte Kenntnisse
  • Databricks
  • Lakeflow
  • Unity Catalog
  • Spark Declarative Pipelines
  • Python
  • Spark
  • Azure DevOps
  • CI/CD
  • Infrastructure as Code
Gehalt (geschätzt, inoffiziell)
~€95k–120k/yrGrober Richtwert für eine Vollzeitstelle; der Arbeitgeber hat keinen Betrag genannt Wie wird das berechnet?

Vollständige Beschreibung

As our Senior Databricks Platform Architect, you will take technical ownership of the evolution of our Databricks platform and help define where we go next. You will work hands‑on with the platform, introduce new capabilities, and ensure it remains secure, scalable, cost‑efficient, and production‑ready.

You will be the technical go‑to person for Databricks, working closely with Data Engineers, Cloud Engineers, Product Owners and external implementation partners. From architecture decisions to troubleshooting complex issues, you will combine technical direction with pragmatic, hands‑on execution.

Databricks evolves quickly, and we want our platform to evolve with it. You will have the opportunity to evaluate new capabilities, decide what makes sense for our environment, and bring the most valuable ones into production.

Your tasks

Building, configuring, and continuously improving our Databricks platform, with a strong focus on scalability, reliability, and production readiness Introducing and implementing new Databricks capabilities, including Lakeflow, Unity Catalog and Spark Declarative Pipelines Developing scalable Python and Spark frameworks and modern data ingestion architectures within Databricks Optimizing the Databricks platform across performance, security, governance, observability, and cost Automating Databricks deployments and platform operations using Azure DevOps, CI/CD, and Infrastructure as Code

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