Senior Data Engineer (ID:3458)

• Act as a Senior Data Engineer, designing, building, and maintaining scalable data solutions on the Azure platform.
• Develop and manage data pipelines using Azure Synapse Pipelines, T-SQL, Synapse Data Warehouse, and Dataflows.
• Transform and prepare enterprise data for analytics and reporting use cases.
• Work extensively with Databricks, using PySpark and SparkSQL for data processing and transformations.
• Convert data sourced from Oracle ERP systems into consumable formats for Power BI and Power Apps reporting.
• Support and contribute to modern data platform initiatives, including exposure to Microsoft Fabric where applicable.
• Ensure data quality, performance optimization, and reliable delivery across data pipelines., * Opportunities to work on enterprise-scale data engineering initiatives using Azure and Databricks.
• Exposure to modern data platforms and analytics-driven reporting solutions.
• A role that combines technical depth with business-facing collaboration.
• An environment that values ownership, independence, and data engineering expertise.
• Involvement in transforming complex ERP data into meaningful business insights.

• 8-10 years of overall experience as a Data Engineer or in a similar data-focused role.
• Solid experience with Databricks, PySpark, and SparkSQL.
• Exposure to Microsoft Fabric is an added advantage.
• Good understanding of Oracle ERP data, particularly Financials, Sales, and HCM domains.
• Experience transforming ERP source data into analytics-ready datasets for reporting platforms such as Power BI and Power Apps.
• Proven ability to work independently while collaborating with cross-functional technical and business teams.

You Should Possess the Ability To

• Design and implement end-to-end data pipelines in Azure-based data platforms.
• Handle large-scale data transformations using Spark-based technologies.
• Translate business requirements into efficient and scalable data models.
• Work autonomously while coordinating effectively with multiple stakeholders.
• Optimize data performance, reliability, and usability for reporting and analytics.
• Adapt to evolving data technologies and support continuous improvement initiatives.