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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 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 organizations evolving data strategy. Required Qualifications Bachelors/Masters degree in Computer Science, Data Science, Information Systems, or a related field. 5+ years of .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.