Senior Data Engineer @

1 week ago

Remote, Czech Republic Dogtronic Solutions Full time

Requirements

  • Strong Python and SQL skills 
  • Proven experience delivering Databricks PySpark pipelines into production 
  • Solid hands-on experience with Databricks 
  • Experience with cloud platforms (Azure, AWS, or GCP) 
  • Comfortable working with large datasets and building robust data pipelines 
  • Clear communication skills – able to explain technical work logically and conceptually, not just technically strong but also strong in client-facing discussions 
  • Senior-level experience (5+ years in data engineering, ideally with Databricks in enterprise settings) 
  • Building and optimizing robust, scalable pipelines for structured and unstructured data. 
  • Experience with data governance, security, and compliance (esp. GDPR/industry standards). 
  • Performance tuning for large-scale datasets (TB/PB). 
  • Familiarity with streaming data (Kafka, Event Hub, Kinesis, or similar). 
  • Full-time capacity, based in the EU 

Nice-to-Have:

  • Experience in banking or insurance sector data projects (risk, compliance, fraud detection, customer analytics) 
  • Exposure to data governance and security best practices (GDPR/financial regulations) 
  • Experience with CI/CD for data pipelines (Azure DevOps, GitHub Actions, Jenkins) 
  • Familiarity with modern data architectures (Lakehouse, streaming data, ML pipelines) 
  • Strong stakeholder management skills — ability to engage with business users and translate requirements into scalable solutions 

As part of the engineering team, you will:

  • Design and implement scalable data pipelines and architectures using Databricks 
  • Work with enterprise clients across sectors (finance, life sciences, technology, etc.), each bringing different data ecosystems and integration requirements. 
  • Collaborate closely with both client teams and internal experts, ensuring best practice delivery on cloud platforms (Azure, AWS or GCP). 
  • This is a chance to combine the stability of a long-term engagement with the variety of project-based delivery building deep expertise in Databricks while continuously facing new real-world data challenges. 
,[] Requirements: Databricks, Data pipelines, Cloud platform, AWS, GCP, Python, SQL, PySpark, Communication skills, Data engineering, Security, ESP, GDPR, Performance tuning, Kafka, Kinesis, DevOps, GitHub Actions, Jenkins, Stakeholder management

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