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Consultant – Data (BI)

🔍 Sri Lanka

New
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Engineer
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186687 Requisition #
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Jun 11, 2025 Post Date

The candidate is responsible for the design, implement, and maintain end-to-end data pipelines using Azure Synapse Analytics, Databricks, and Azure Data Lake. Optimize data processing and transformation workflows for high-performance analytics. Build, deploy, and manage Power BI reports, dashboards, and data visualizations that provide actionable insights for stakeholders. Work with data architects and analysts to understand business requirements and translate them into robust data solutions. Implement best practices for data governance, security, and compliance across Azure Data Lake and related technologies. Monitor, troubleshoot, and optimize the performance of data pipelines, ensuring high availability and reliability. Lead initiatives for the migration and integration of on-premises data systems to Azure cloud-based solutions. Drive the adoption of DevOps principles in data engineering to automate deployment and ensure continuous integration/continuous delivery (CI/CD) practices.

  • Bachelor’s degree in computer science, Information Technology, Data Science, or related field.
  • 5+ years of experience in data engineering and visualization, with at least 3 years of hands-on experience working with Power BI, Azure Synapse Analytics, Azure Data Lake, Databricks and Azure Data Factory (ADF).
  • Proficient in Python, SQL for building data transformation and ETL pipelines.
  • Strong knowledge of Power BI, including creating and publishing interactive dashboards, reports, and data models.
  • Extensive knowledge of Azure Synapse Analytics for data warehousing, querying, and large-scale analytics.
  • Strong knowledge of cloud data architecture and experience with building data lakes, data warehouses, and real-time data pipelines in Azure.
  • Advanced experience with Databricks and Apache Spark for big data processing, transformation, and machine learning workflows.
  • Solid understanding of data governance, data quality, and security best practices in cloud-based environments.
  • Familiarity with DevOps practices and tools such as Azure DevOps, Git, and CI/CD pipelines for data engineering workflows.
  • Solid understanding of data modeling, ETL processes, and big data technologies.
  • Strong problem-solving skills and the ability to troubleshoot complex data issues.
  • Excellent communication skills, with the ability to present technical concepts to non-technical stakeholders.

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