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

Key Responsibilities:

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.