Lead Data Engineer

About US:-

We turn customer challenges into growth opportunities.

Material is a global strategy partner to the world's most recognizable brands and innovative companies. Our people around the globe thrive by helping organizations design and deliver rewarding customer experiences.

We use deep human insights, design innovation and data to create experiences powered by modern technology. Our approaches speed engagement and growth for the companies we work with and transform relationships between businesses and the people they serve.

Srijan, a Material company, is a renowned global digital engineering firm with a reputation for solving complex technology problems using their deep technology expertise and leveraging strategic partnerships with top-tier technology partners. Be a part of an Awesome Tribe

Job Responsibilities:

Design and Develop Data Pipelines:
Development and optimisation of scalable data pipelines within Microsoft Fabric, leveraging fabric based notebooks, Dataflows Gen2, Data Pipelines, and Lakehouse architecture. Build robust pipelines using both batch and real-time processing techniques. Integrate with Azure Data Factory or Fabric-native orchestration for seamless data movement.

Microsoft Fabric Architecture:
Work with the Data Architecture team to implement scalable, governed data architectures within OneLake and Microsoft Fabric's unified compute and storage platform. Align models with business needs, promoting performance, security, and cost-efficiency.

Data Pipeline Optimisation:
Continuously monitor, enhance, and optimise Fabric pipelines, notebooks, and lakehouse artifacts for performance, reliability, and cost. Implement best practices for managing large-scale datasets and transformations in a Fabric-first ecosystem.

Collaboration with Cross-functional Teams:
Work closely with analysts, BI developers, and data scientists to gather requirements and deliver high-quality, consumable datasets. Enable self-service analytics via certified and reusable Power BI datasets connected to Fabric Lakehouses.

Documentation and Knowledge Sharing:
Maintain clear, up-to-date documentation for all data pipelines, semantic models, and data products. Share knowledge of Fabric best practices and mentor junior team members to support adoption across teams.

Microsoft Fabric Platform Expertise:
Use your expertise in Microsoft Fabric, including Lakehouses, Notebooks, Data Pipelines, and Direct Lake, to build scalable solutions integrated with Business Intelligence layers, Azure Synapse, and other Microsoft data services.

Required Skills and Qualifications:

• Experience in Microsoft Fabric / Azure Eco System : 7 years working with Azure eco system , Relavent experience in Microsoft Fabric, including Lakehouse,oine lake, Data Engineering, and Data Pipelines components.
• Proficiency in Azure Data Factory and/or Dataflows Gen2 within Fabric for building and orchestrating data pipelines.
• Advanced Data Engineering Skills: Extensive experience in data ingestion, transformation, and ELT/ETL pipeline design. Ability to enforce data quality, testing, and monitoring standards in cloud platforms.
• Cloud Architecture Design: Experience designing modern data platforms using Microsoft Fabric, OneLake, and Synapse or equivalent.
• Strong / Indeapth SQL and Data Modelling: Expertise in SQL and data modelling (e.g., star/snowflake schemas) for Data intergation / ETL, reporting and analytics use cases.
• Collaboration and Communication: Proven ability to work across business and technical teams, translating business requirements into scalable data solutions.
• Cost Optimisation: Experience tuning pipelines and cloud resources (Fabric, Databricks, ADF) for cost-performance balance.

Preferred Skills:

• Deep understanding of Azure, Microsoft Fabric ecosystem, including Power BI integration, Direct Lake, and Fabric-native security and governance.
• Familiarity with OneLake, Delta Lake, and Lakehouse architecture as part of a modern data platform strategy.
• Experience using Power BI with Fabric Lakehouses and DirectQuery/Direct Lake mode for enterprise reporting.
• Working knowledge of PySpark, strong SQL, and Python scripting within Fabric or Databricks notebooks.
• Understanding of Microsoft Purview, Unity Catalog, or Fabric-native governance tools for lineage, metadata, and access control.
• Experience with DevOps practices for Fabric or Power BI, including version control, deployment pipelines, and workspace management.
• Knowledge of Azure Databricks: Familiarity with building and optimising Spark-based pipelines and Delta Lake models as part of a modern data platform. is an added advantage.