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Job Description: Job Description: Data Engineer Databricks & Microsoft Fabric Location : Chennai, India Experience : 6 to 8 years Role Summary We are looking for an experienced Data Engineer with strong hands-on expertise in Databricks, Microsoft Fabric, PySpark, SQL, and cloud-based data engineering. The candidate will be responsible for designing, developing, and optimizing scalable data pipelines, lakehouse solutions, and analytics-ready data models. The role requires strong experience in building end-to-end data engineering solutions, working with structured and semi-structured data, implementing data quality controls, and supporting enterprise reporting and analytics platforms. Key Responsibilities Data Engineering and Pipeline Development Design, build, and maintain scalable data pipelines using Databricks, PySpark, Spark SQL, and Microsoft Fabric.Develop batch and incremental data ingestion pipelines from multiple source systems.Build and optimize Bronze, Silver, and Gold layer data models using lakehouse architecture.Implement ELT/ETL workflows for data transformation, enrichment, validation, and publishing.Work with structured, semi-structured, and unstructured data formats such as CSV, Parquet, JSON, Delta, and XML. Databricks Development Develop notebooks, jobs, workflows, and reusable components in Azure Databricks.Implement Delta Lake features such as schema evolution, merge/upsert, time travel, and optimized storage.Optimize Spark jobs for performance, scalability, and cost efficiency.Implement partitioning, caching, indexing, and cluster optimization strategies.Troubleshoot job failures, performance bottlenecks, and data quality issues. Microsoft Fabric Development Build data solutions using Microsoft Fabric Lakehouse, Warehouse, Data Factory, Pipelines, Notebooks, and OneLake.Develop and manage data pipelines in Fabric for ingestion, transformation, and orchestration.Work with Fabric SQL endpoints, semantic models, and Power BI integration.Support migration or modernization of existing data platforms into Microsoft Fabric.Implement reusable data engineering patterns and framework-based development in Fabric. Data Quality, Governance, and Security Implement data validation, reconciliation, exception handling, and audit controls.Define and apply data quality rules including null checks, duplicate checks, referential checks, and cross-field validations.Maintain data lineage, metadata, source-to-target mapping, and technical documentation.Ensure data pipelines comply with enterprise security, access control, and governance standards.Support integration with data governance tools such as Microsoft Purview, where applicable. DevOps and Production Support Implement CI/CD practices for notebooks, pipelines, SQL scripts, and configuration files.Use Git-based version control and deployment processes across environments.Monitor production jobs and resolve incidents within agreed timelines.Prepare runbooks, deployment guides, operational support documents, and handover materials.Collaborate with architects, business analysts, data analysts, and reporting teams to deliver reliable data solutions. Required Skills Technical Skills Strong hands-on experience in Azure Databricks.Strong experience in Microsoft Fabric components such as Lakehouse, Warehouse, Data Factory, Pipelines, Notebooks, and OneLake.Proficiency in PySpark, Spark SQL, Python, and SQL.Strong knowledge of Delta Lake, lakehouse architecture, and medallion architecture.Experience with cloud storage and data platforms, preferably Azure Data Lake Storage, Azure SQL, Synapse, or Fabric OneLake.Experience in data ingestion from databases, APIs, files, SFTP, cloud storage, and streaming sources.Good understanding of data modeling, dimensional modeling, and analytics-ready data structures.Experience with performance tuning of Spark jobs and SQL queries.Experience in job scheduling, monitoring, logging, and error handling.Knowledge of CI/CD, Git, Azure DevOps, and deployment automation. Preferred Skills Experience with Power BI and semantic model integration.Experience in migrating workloads from legacy ETL tools, Synapse, ADF, or Databricks to Microsoft Fabric.Knowledge of Microsoft Purview for data cataloging, lineage, and governance.Experience in building reusable data engineering frameworks.Exposure to real-time or near-real-time data processing.Azur .
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.