Senior Data Engineer - Microsoft Fabric

At ADROSONIC, we are looking for a highly skilled Senior Data Engineer

• Microsoft Fabric with 5+ years of hands-on experience in designing, building, and optimizing modern data platforms. The ideal candidate will bring strong expertise in Microsoft Fabric, Azure data services, and advanced data modelling practices . This role demands deep technical strength in data engineering along with mandatory data modelling expertise to design scalable, analytics-ready, and high-performing data solutions. The candidate should possess a collaborative mindset and work seamlessly with BI teams, architects, stakeholders, and clients to deliver enterprise-grade data platforms. You will play a key role in building modern cloud-based data architectures, enabling seamless reporting in Power BI, and ensuring efficient, reliable, and optimized data pipelines across projects.

Requirements

Microsoft Fabric Data Platform Development:

• Design and implement scalable data solutions using Microsoft Fabric (Lakehouse, Data Warehouse, Data Factory, Notebooks).
• Architect hybrid data solutions integrating Microsoft Fabric with Azure Synapse Analytics and Azure SQL Managed Instance.
• Build and manage Medallion architecture (Bronze, Silver, Gold layers) within OneLake.
• Integrate Microsoft Fabric with Azure services such as Azure Data Lake Storage Gen2 to build scalable, secure, and performance-optimized data frameworks.
• Develop efficient ingestion, transformation, and loading pipelines.
• Optimize Fabric workloads for performance, scalability, and cost efficiency.
• Implement secure connectivity using Azure Private Endpoints and VNet integration where required. Data Modeling & Warehousing :
• Design conceptual, logical, and physical data models.
• Implement dimensional modeling techniques (Star Schema, Snowflake Schema).
• Develop well-structured fact and dimension tables optimized for analytical workloads.
• Ensure data models are optimized for Power BI and enterprise reporting.
• Maintain consistency, scalability, and performance across evolving data models.
• Design data models using different database schemas (Kimball, Star, Snowflake) for optimal data retrieval and storage.
• Ensure models are optimized for both transactional (OLTP) and analytical (OLAP) workloads, using best practices in database design. End-to-End Data Engineering:
• Develop ETL/ELT pipelines using Fabric Data Factory, Azure Data Factory, and related Azure services.
• Integrate structured and unstructured data from Azure SQL Database, Azure SQL Managed Instance, Azure Data Lake Gen2, REST APIs, and external sources.
• Implement transformation logic using SQL, PySpark, or Spark frameworks.
• Leverage Azure Databricks for advanced data processing where required.
• Ensure data validation, quality checks, and reliability within pipelines.
• Implement secure credential management using Azure Key Vault. BI Collaboration & Analytical Enablement:
• Work closely with BI developers to design analytics-ready datasets.
• Ensure seamless integration between Microsoft Fabric and Power BI.
• Support backend optimization to improve dashboard performance.
• Act as a technical bridge between Data Engineering and BI teams. Stakeholder & Client Collaboration
• Collaborate seamlessly with internal stakeholders and external clients.
• Gather, analyze, and translate business requirements into scalable technical solutions.
• Clearly communicate data architecture decisions, pipeline designs, and modeling approaches.
• Participate in client workshops, technical discussions, and solution presentations.
• Ensure strong alignment between business objectives and delivered data solutions. Performance Optimization & Reliability
• Monitor data pipeline performance using Azure Monitor & Log Analytics and resolve bottlenecks proactively.
• Optimize query performance across Fabric Warehouse, Azure Synapse, and Azure SQL MI environments.
• Implement logging, monitoring, and alerting mechanisms.
• Ensure high availability, reliability, and timely delivery of data.
• Continuously improve scalability and maintainability of data platforms. Best Practices & Standards
• Follow data engineering standards, naming conventions, and documentation practices.
• Implement version control and CI/CD processes for data pipelines.
• Promote reusable components and clean coding practices.
• Ensure adherence to security standards and basic data governance principles. Performance Optimization & Maintenance:
• Continuously monitor and optimize the performance of data models, making improvements to ensure efficiency and scalability.
• Conduct regular audits to ensure that the data models remain aligned with the organization’s evolving data strategy. Required Qualifications:
• Bachelor’s/Master’s degree in Computer Science, Data Science, Information Systems, or a related field.
• 5+ years of hands-on experience in Data Engineering.
• Strong expertise in Microsoft Fabric ecosystem and Azure data services including Azure SQL Database, Azure SQL Managed Instance, Azure Synapse Analytics, Azure Data Lake Storage Gen2, and Azure Data Factory.
• Mandatory strong experience in Data Modeling (conceptual, logical, physical) with solid knowledge of dimensional modeling (Star/Snowflake schema) and data warehousing principles.
• Advanced proficiency in SQL, including performance optimization and complex transformations and experience with NoSQL Databases.
• Hands-on experience in designing ETL/ELT pipelines using Fabric, Azure Data Factory, or Azure Databricks, with working knowledge of PySpark/Spark.
• Good understanding of data governance fundamentals, data quality, metadata management, and exposure to compliance standards.
• Strong analytical, troubleshooting, and stakeholder collaboration skills, with experience working in Agile delivery environments and version-controlled setups.

Preferred Qualifications

• Microsoft certifications such as DP-700 (Microsoft Fabric Data Engineer Associate)
• Microsoft Azure certifications (e.g., Azure Data Engineer Associate).
• Domain experience in Insurance and Financial Services is a strong plus.
• Experience in client-facing or consulting environments.
• Exposure to DevOps practices and Git-based version control.
• Experience handling enterprise-scale data environments. Soft Skills:
• Strong collaborative mindset.
• Seamless coordination with BI teams and stakeholders.
• Strong client communication and stakeholder management skills.
• Ownership-driven and proactive approach.
• Strong analytical and problem-solving abilities.
• Ability to work effectively in fast-paced, evolving environments.