Data Engineer

Key Responsibilities:

• Design, develop, and optimize scalable data pipelines using Databricks (PySpark, Scala, SQL).
• Implement ETL/ELT workflows for large-scale data integration across cloud and on-premise environments.
• Leverage Microsoft Fabric (Data Factory, OneLake, Lakehouse, DirectLake, etc.) to build unified data solutions.
• Collaborate with data architects, analysts, and stakeholders to deliver business-critical data models and pipelines.
• Monitor and troubleshoot performance issues in data pipelines.
• Ensure data governance, quality, and security across all data assets.
• Work with Delta Lake, Unity Catalog, and other modern data lakehouse components.

• Automate and orchestrate workflows using Azure Data Factory, Databricks Workflows, or Microsoft Fabric pipelines.

• Participate in code reviews, CI/CD practices, and agile ceremonies.

Required Skills:

• 5–7 years

  of experience in data engineering, with

  strong exposure

  to

  Databricks

  .
• Proficient in PySpark, SQL, and performance tuning of Spark jobs.

• Hands-on experience

  with

  Microsoft Fabric components

  .
• Experience with Azure Synapse, Data Factory, and Azure Data Lake.
• Understanding of Lakehouse architecture and modern data mesh principles.

• Familiarity with Power BI integration and semantic modeling (preferred).

• Knowledge of DevOps, CI/CD for data pipelines (e.g., using GitHub Actions, Azure DevOps).

• Excellent problem-solving, communication, and collaboration skills.

Key Responsibilities:

• Design, develop, and optimize scalable data pipelines using Databricks (PySpark, Scala, SQL).
• Implement ETL/ELT workflows for large-scale data integration across cloud and on-premise environments.
• Leverage Microsoft Fabric (Data Factory, OneLake, Lakehouse, DirectLake, etc.) to build unified data solutions.
• Collaborate with data architects, analysts, and stakeholders to deliver business-critical data models and pipelines.
• Monitor and troubleshoot performance issues in data pipelines.
• Ensure data governance, quality, and security across all data assets.
• Work with Delta Lake, Unity Catalog, and other modern data lakehouse components.

• Automate and orchestrate workflows using Azure Data Factory, Databricks Workflows, or Microsoft Fabric pipelines.

• Participate in code reviews, CI/CD practices, and agile ceremonies.

Required Skills:

• 5–7 years

  of experience in data engineering, with

  strong exposure

  to

  Databricks

  .
• Proficient in PySpark, SQL, and performance tuning of Spark jobs.

• Hands-on experience

  with

  Microsoft Fabric components

  .
• Experience with Azure Synapse, Data Factory, and Azure Data Lake.
• Understanding of Lakehouse architecture and modern data mesh principles.

• Familiarity with Power BI integration and semantic modeling (preferred).

• Knowledge of DevOps, CI/CD for data pipelines (e.g., using GitHub Actions, Azure DevOps).

• Excellent problem-solving, communication, and collaboration skills.