Lead Azure Fabric Engineer

Absolutelabs; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Absolutelabs is seeking a data engineer to design and deliver end-to-end data pipelines using Microsoft Fabric and Azure Data Factory. You will build and manage data models across OneLake and Fabric Workspaces, and develop complex SQL, PySpark, and DAX queries for large-scale analytics.

The role involves integrating Fabric workloads with ADLS Gen2, Synapse, Databricks, and Power BI, implementing medallion architecture, and ensuring data governance and SDLC compliance.

Qualifications

• Must have hands-on experience with Azure Data Factory (ADF) and Microsoft Fabric.
• Extensive experience building end-to-end data pipelines in Fabric ecosystem (OneLake, Workspaces).
• Strong SQL, PySpark, and DAX for large-scale data processing and analytics.
• Experience integrating Fabric workloads with ADLS Gen2, Azure Synapse, Databricks, and Power BI.
• Knowledge of medallion architecture (Bronze/Silver/Gold) and SDLC best practices.
• Ability to translate business requirements into scalable data solutions and reusable data assets.

Responsibilities

• Design and develop end-to-end data pipelines using Microsoft Fabric and ADF.
• Build and manage data models across OneLake and Fabric Workspaces.
• Develop and optimise SQL, PySpark, and DAX queries for large-scale workloads.
• Integrate Fabric workloads with ADLS Gen2, Synapse, Databricks, and Power BI.
• Implement medallion architecture and SDLC practices for data quality and releases.
• Collaborate with stakeholders to translate requirements into scalable data assets.
• Ensure data governance and lineage with Purview and Fabric governance features.
• Monitor, troubleshoot, and optimize data workflows for performance and cost (FinOps).
• Participate in CI/CD pipelines for data workloads using Azure DevOps and Git.

Skills

Fabric Suite

ADF

Data Modeling

SQL

PySpark

DAX

Power BI

Medallion Architecture

SDLC

Tools

Azure DevOps

Git

Notebooks

OneLake

Job description

Role & responsibilities

• Design and develop end-to-end data pipelines using Microsoft Fabric (Data Factory, Dataflows Gen2, Notebooks, Pipelines) and Azure Data Factory (ADF). Build and manage models in the Microsoft Fabric ecosystem.
• Build and manage data models across OneLake and Fabric Workspaces, ensuring well-structured environments, access, and collaboration across teams.
• Develop and optimise complex SQL, PySpark, and DAX queries for large-scale data processing and analytics workloads.
• Integrate Fabric workloads with Azure services including Azure Data Lake Storage (ADLS Gen2), Azure Synapse Analytics, Azure Databricks, and Power BI.
• Implement medallion architecture and data modelling best practices, following standard SDLC practices to ensure data quality, consistency, and reliable releases.
• Collaborate with stakeholders to translate business requirements into scalable data solutions and reusable data assets.
• Ensure data governance, lineage tracking, and compliance using Microsoft Purview and Fabric's built-in governance features.
• Monitor, troubleshoot, and performance-tune data workflows, keeping an eye on capacity usage and cost efficiency (FinOps) while ensuring SLAs are met.
• Participate in CI/CD pipeline setup for data workloads using Azure DevOps and Git-based version control.

Preferred candidate profile

• Design and develop end-to-end data pipelines using Microsoft Fabric (Data Factory, Dataflows Gen2, Notebooks, Pipelines); hands-on Azure Data Factory (ADF) experience is mandatory.
• Build and manage data models within the Microsoft Fabric ecosystem, including OneLake and Fabric Workspace setup and management.
• Develop and optimise complex SQL, PySpark, and DAX queries for large-scale data processing and analytics workloads.
• Integrate Fabric workloads with Azure services, including Azure Data Lake Storage (ADLS Gen2), Azure Synapse Analytics, Azure Databricks, and Power BI.
• Implement medallion architecture (Bronze / Silver / Gold) and data modelling best practices, with a good grasp of SDLC processes to ensure data quality and consistency.
• Collaborate with stakeholders to translate business requirements into scalable data solutions and reusable data assets.