Data Engineer - Microsoft Fabric / Azure - Intermediate

Job Title: Data Engineer Microsoft Fabric / Azure

Client: Autodesk

Interview: In-Person/Oniste

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.