Data Engineer

Role Overview:

Experience Above : 7 to 12 years

We are building an accelerator for migrating workloads from Azure Synapse and Azure Data Factory (ADF) to Microsoft Fabric. We are looking for a highly skilled Data Engineer with strong hands-on expertise in Microsoft Fabric, Synapse, ADF, and Databricks to drive design, development, and implementation of scalable migration solutions.

Key Responsibilities:

• Design and develop migration frameworks/accelerators for moving data workloads from Azure Synapse and ADF to Microsoft Fabric.

• Analyze existing architectures and define optimal migration strategies, including data pipelines, transformations, and storage layers.

• Build and optimize data ingestion, transformation, and orchestration pipelines using Fabric, ADF, and Databricks.

• Develop reusable components, templates, and best practices for large-scale migrations.

• Collaborate with cross-functional teams (architecture, presales, delivery) to define solution approaches and estimates.

• Ensure performance optimization, cost efficiency, and scalability of migrated solutions.

• Implement data governance, security, and monitoring frameworks within Fabric.

• Provide technical leadership, mentorship, and code reviews for engineering teams.

Required Skills & Experience:

Core Technologies:

• Strong hands-on experience with Microsoft Fabric (Lakehouse, Data Factory, Pipelines, Notebooks).

• Deep expertise in Azure Synapse Analytics (SQL pools, Spark pools, pipelines).

• Proven experience with Azure Data Factory (ADF) for orchestration and ETL/ELT pipelines.

• Strong working knowledge of Databricks (Delta Lake, notebooks, job orchestration).

Programming & Data Skills:

• Proficiency in PySpark and Python for data transformations.

• Advanced SQL skills with strong problem-solving ability.

• Experience with data modeling (star/snowflake schemas, medallion architecture).

Data Engineering & Architecture:

• Strong understanding of data engineering principles, distributed data processing, and big data frameworks.