Data Engineer

Job Title: Data Engineer – Microsoft Fabric / Azure Client: Autodesk Interview: In-Person/Onsite Work Model: Remote Must Haves - 7+ years of experience in data engineering and enterprise data integration. - Strong hands-on experience with Microsoft Fabric and related Azure data services. - Strong experience with PySpark, SparkSQL, Python, and SQL/T-SQL. - Experience building ingestion and transformation pipelines from APIs, files, databases, and third-party platforms. - Experience with curated data layers, data modeling, and analytics-ready datasets. - Experience tuning data pipelines for scale, resiliency, and cost optimization. - Working knowledge of security, access controls, and data governance practices. - Experience with Azure DevOps and Git-based deployment automation. Description / Responsibilities / Skills Role Overview - Requiring a senior engineer who can work independently while collaborating with data architects, analysts, application teams, and business stakeholders. Experience & Skill Set Requirements - Preferred / Nice-to-Have Skills - Experience with Microsoft Fabric Lakehouse and Warehouse architectures. - Experience with OneLake. - Experience with Fabric Data Factory / Data Pipelines. - Experience with Azure Data Factory. - Experience with Azure Data Lake Storage (ADLS Gen2). - Experience with Delta Lake / Delta tables. - Knowledge of medallion architecture – Bronze, Silver, and Gold data layers. - Experience supporting enterprise analytics or reporting platforms such as Power BI. - Experience designing reusable ingestion frameworks and metadata-driven pipelines. - Understanding of data observability, lineage, metadata management, and governance frameworks. - Experience working within large enterprise or SaaS/product technology environments. Required Skills & Qualifications – Additional Criteria - Strong understanding of ETL/ELT architecture and data integration patterns. - Experience implementing data-quality checks, error handling, monitoring, and recovery mechanisms. - Strong troubleshooting and analytical skills. - Ability to work effectively in a distributed, fully remote engineering environment. Key Responsibilities - Design, develop, and maintain scalable enterprise data pipelines using Microsoft Fabric and Azure data services. - Build ingestion frameworks for structured and semi-structured data from REST APIs, flat files, relational databases, cloud platforms, third-party applications and enterprise systems. - Develop complex data transformation and processing workflows using PySpark, SparkSQL, Python, SQL, and T-SQL. - Build and maintain raw, curated, and analytics-ready data layers supporting reporting, analytics, and downstream applications. - Design efficient data models and datasets optimized for analytical consumption. - Implement data quality, validation, reconciliation, and monitoring mechanisms across ingestion and transformation pipelines. - Troubleshoot pipeline failures, performance bottlenecks, data-quality issues, and integration problems. - Collaborate with architects and business teams to translate data requirements into scalable technical solutions. - Support production deployments, monitoring, troubleshooting, and continuous improvement of the data platform. - Document data flows, transformation logic, technical designs, dependencies, and operational procedures.