Senior Data Engineer

Responsibilities

• Design, build, and maintain enterprise-scale lakehouse architectures using Microsoft Fabric
• Architect end-to-end data engineering solutions on Azure, including ingestion, transformation, storage, and serving layers
• Build and optimize data pipelines using Fabric Data Factory, Dataflows, and Notebooks (Spark)
• Design and manage OneLake, Lakehouse, and Warehouse artifacts within Microsoft Fabric
• Develop scalable ETL/ELT pipelines integrating structured and unstructured data from diverse enterprise sources
• Implement data modeling best practices (dimensional modeling, medallion architecture — bronze/silver/gold layers)
• Collaborate with business and analytics teams across retail, financial services, FMCG, and utilities to understand domain-specific data needs
• Ensure data quality, governance, security, and compliance across the platform (row-level security, sensitivity labels, Purview integration)
• Optimize pipeline performance, cost, and reliability for large-scale enterprise datasets
• Partner with architects and stakeholders to define data platform strategy and roadmap
• Mentor junior data engineers and contribute to engineering best practices and standards
• Support migration of legacy data warehouse/ETL solutions to modern lakehouse architecture on Azure/Fabric

Requirements

• 7–10 years of experience in data engineering and analytics, with significant hands-on experience in Azure Data Engineering
• Strong, demonstrable expertise in Microsoft Fabric — Lakehouse, Data Factory, Dataflows Gen2, Notebooks, OneLake, Power BI integration
• Deep understanding of lakehouse architecture and medallion (bronze/silver/gold) design patterns
• Proficiency with Azure data services: Azure Data Factory, Azure Synapse Analytics, Azure Databricks, ADLS Gen2
• Strong SQL and Spark (PySpark/Scala) skills for large-scale data transformation
• Experience delivering enterprise-grade data solutions in one or more of: retail, financial services, FMCG, or utilities
• Solid understanding of data governance, security, and compliance frameworks (Microsoft Purview, RBAC, data masking)
• Experience with CI/CD for data pipelines (Azure DevOps, Git-based deployment)
• Strong grasp of data modeling techniques (star schema, slowly changing dimensions, etc.)
• Excellent stakeholder management and communication skills, with experience working directly with business and analytics teams
• Ability to translate ambiguous business requirements into scalable technical solutions

Nice to Have

• Microsoft certifications (e.g., DP-600 Fabric Analytics Engineer, DP-203 Azure Data Engineer)
• Experience with Power BI semantic models and direct lake mode
• Exposure to Azure Purview for data cataloging and lineage
• Experience with real-time data processing (Event Streams in Fabric, Azure Event Hubs)
• Prior consulting or client-facing experience delivering solutions across multiple industry domains

This Senior Data Engineer role focuses on building enterprise lakehouse platforms with Microsoft Fabric and Azure. You’ll develop data pipelines and architectures, support governance and migration work, and partner with business and analytics teams to deliver data solutions across several industries.

Role at a glance

Skills mentioned

What you will likely work on

• Design and maintain enterprise-scale lakehouse architectures and data platform layers on Microsoft Fabric and Azure.
• Build ingestion, transformation, storage, and serving workflows, including ETL/ELT pipelines for structured and unstructured data.
• Develop and tune Fabric pipelines, Dataflows, and Spark notebooks, with attention to performance, cost, and reliability.
• Model data using dimensional techniques and bronze, silver, and gold medallion layers.
• Apply data quality, governance, security, and compliance controls, including access policies and sensitivity labeling.
• Work with business and analytics teams to turn domain needs and ambiguous requirements into technical solutions.
• Help define platform strategy and roadmap, and contribute to engineering standards while mentoring junior data engineers.
• Support migration of legacy warehouse and ETL workloads to Azure and Fabric lakehouse architectures.

Good fit signals

• Has 7–10 years in data engineering and analytics, including substantial hands-on Azure data engineering work.
• Can demonstrate deep Microsoft Fabric capability across its lakehouse, pipeline, notebook, and OneLake components.
• Has delivered enterprise-grade solutions in at least one of retail, financial services, FMCG, or utilities.
• Brings strong SQL, Spark, data modeling, and governance knowledge for large-scale enterprise workloads.
• Communicates effectively with business and analytics stakeholders; relevant Microsoft certifications or client-facing experience are helpful, not required.