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Fabric Data Engineer

Adastra

Nuevo
Senior 🇬🇧 English
Microsoft Fabric Azure Databricks SQL PySpark Delta Lake Git Azure Monitor Prometheus Grafana dbt Great Expectations

Descripcion del puesto

About the role

A Fabric Data Engineer is sought to develop and support data pipelines and transformation logic on the data platform. You will build ingestion jobs, author SQL and PySpark notebooks, and run data quality and validation checks across lakehouse and warehouse layers. Working under the guidance of senior engineers and the Fabric Architect, you will also help with monitoring, documentation and issue triage.

Key responsibilities

  • Develop, test and maintain ingestion and transformation pipelines on Microsoft Fabric (lakehouse and warehouse layers).
  • Author clear, maintainable SQL and PySpark notebooks for ETL/ELT jobs and transformations.
  • Implement automated data quality and validation checks, and investigate and remediate data issues.
  • Monitor pipeline health, respond to alerts and perform first-line triage and incident investigation.
  • Follow platform and engineering standards, contribute to runbooks and operational documentation.
  • Collaborate with data engineers, analysts and product stakeholders to turn requirements into reliable data solutions.
  • Contribute to CI/CD, testing and deployment of data pipelines.
  • Improve pipeline performance, reliability and observability through instrumentation and refactoring.

Required profile

  • 5+ years of professional experience in data engineering or a related role.
  • Bachelor's degree in Computer Science, Engineering, Data Science or equivalent practical experience.
  • Good troubleshooting skills and experience with monitoring and alerting for data pipelines.

Required skills

  • Solid experience with Microsoft Azure services, including Microsoft Fabric and Azure Databricks.
  • Strong SQL for data modelling, transformation and validation.
  • PySpark and notebook-based data processing.
  • Lakehouse architectures and data warehouse concepts (ETL/ELT patterns).
  • Data quality checks and debugging across pipeline layers.
  • Version control (Git) and collaborative documentation.
  • Nice to have: Delta Lake, Airflow or Azure Data Factory, data testing frameworks (Great Expectations, dbt tests), observability tools (Azure Monitor, Prometheus, Grafana), and Fabric or Azure certifications.

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Publicado hace 16 horas

Expira en 1 mes

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