Job Description

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.