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Link Group

2 months ago

Senior Data Engineer @ Link Group

  • Polska
  • Full Time
  • 240000.00 - 324000.00
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Company information

Company
Link Group
Location
Polska Poland
Posted
2 months ago
View all jobs at Link Group

Job description

  • Minimum 3 years of experience in Data Engineering.
  • Experience in designing and maintaining production data pipelines.
  • Very good knowledge of: Python, PySpark, SQL, Terraform, YAML, Databricks Asset Bundles.
  • Experience with: Apache Spark, Databricks, Delta Lake, working with large data volumes.
  • Very good knowledge of Microsoft Azure services, in particular: Azure Data Factory, ADLS, Azure Databricks, Unity Catalog, Azure DevOps, Azure Event Hub.
  • Experience with Apache Airflow, CI/CD (Azure DevOps Repos & Pipelines), and Lakehouse architecture and Medallion Architecture.
  • Knowledge of: Data Quality, Data Governance, data modeling, and access control in Unity Catalog.
  • Ability to translate business requirements into technical solutions.
  • Experience in code review, testing, deployment automation, and solution documentation.
  • Readiness to act as a technical lead and support the team in terms of standards and best practices.
Nice to have
  • Experience with Databricks LakeFusion, Microsoft Fabric (Data Engineering).
  • Knowledge of Databricks DQX or other data quality management tools.
  • Databricks or Data Engineering certifications.
We are looking for a Data Engineer who will be responsible for designing and developing the Data Platform and scalable data solutions. This is a role that combines advanced data engineering with real impact on the platform architecture, technical standards, and its development direction. [Designing and developing the Data Platform in a Lakehouse architecture. Participating in architectural decisions regarding the Data Platform and data integration. Designing and developing ELT processes for reporting, analytics, and AI/ML. Creating data models and analytical layers. Optimizing solutions for performance, cost, and scalability. Defining Data Engineering standards, automation, and best practices. Ensuring data quality, security, and compliance. Collaborating with business, architects, and IT teams in solution design. Acting as a technical lead in Data Platform initiatives.] Requirements: Data engineering, Python, PySpark, SQL, Terraform, YAML, Databricks Asset Bundles, Apache Spark, Databricks, Delta Lake, Microsoft Azure, Azure Data Factory, ADLS, Azure Databricks, Unity Catalog, Azure DevOps, Azure Event Hub, Apache Airflow, CI/CD, Azure DevOps Repos and Pipelines, Lakehouse, Medallion Catalog, Code review, Testing, Automation, Databricks LakeFusion, Microsoft Fabric, Databricks DQX, Databricks certification, Data Engineering certificate

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