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 Title:

Lead Data Engineer (Microsoft Azure , Synapse Data Engineering (Fabric))

Job Responsibilities:

Design and Develop Data Pipelines:

Hands on with development, and optimisation of scalable and reusable - data pipelines in

Azure Microsoft Fabric Synapse Data Engineering

, leveraging both

batch

and

real-time processing techniques

. Ensure smooth integration with

Azure Data Factory

for orchestration and workflow management.

Cloud Data Architecture:

Collaborate with the

Data Architecture team

to design and implement robust data architectures in the

Azure environment

, ensuring they align with business needs while optimising performance, scalability, and cost-efficiency.

Pipeline Optimisation :

Continuously monitor and optimise the performance, cost, and reliability of data pipelines, ensuring efficient processing, storage, and management of large datasets.

Cross-functional Collaboration:

Work closely with

data engineering teams

,

analysts

, and

business stakeholders

to understand data requirements, developing solutions that enable self-service analytics and support the decision-making process.

Documentation & Knowledge Sharing:

Contribute to internal documentation, fostering a culture of knowledge-sharing. Provide

mentorship

and guidance to junior engineers, helping to elevate team skills and improve overall team performance.

Microsoft Fabric Experience:

Apply your knowledge of

Azure Tech Stack

on Data Engineering (

or your willingness to learn

)

Fabric-based development

to manage end-to-end data orchestration, governance, and security across cloud and on-premises systems, ensuring seamless data movement and integration across hybrid environments.

Data Modelling Expertise:

Leverage your deep expertise in

Azure

to design and implement

data models

, create processing pipelines, and integrate with other

Azure services

like

Data Lake

and

Synapse

to support data storage and analytics needs.

Required Skills and Qualifications:

Experience with Azure Ecosystem (Preferably Synapse):

5+ years of hands-on experience with

Azure Ecosystem

, including

Synapse

,

Spark

,

OneLake

, and other Fabric tools. Expertise in optimising

Fabric notebooks

and efficiently managing large-scale data workloads.

Proficiency in Azure Data Factory:

Strong experience with designing and orchestrating complex data pipelines using

Azure Data Factory

, with an emphasis on seamless data flow integration across various Azure services.

Familiarity with Microsoft Fabric:

A working knowledge or eagerness to learn

Azure Data Fabric

, focusing on cross-platform data orchestration, governance, and security.

Advanced Data Engineering Skills:

Extensive experience in data engineering, including the design and implementation of

ETL processes

and working with large datasets. Proven expertise in

data quality

, monitoring, and testing practices.

Cloud Architecture Design Expertise:

Experience designing and implementing

data architectures

in the

Azure ecosystem

, including tools such as

Data Lake

,

Synapse

, and

Azure Storage

.

SQL and Data Modelling Expertise:

Strong skills in

SQL

and

data modelling

, with the ability to design optimised data structures, tables, and views. Knowledge of both transactional and analytical data modelling.

Collaboration and Communication Skills:

Strong ability to work cross-functionally with teams from various domains. Ability to communicate complex technical concepts to both technical and non-technical stakeholders.

Cost Optimisation:

Proven experience optimising

data engineering processes

and

Azure resources

for both performance and cost, particularly in large-scale cloud environments.

Preferred Skills:

Data Lakehouse Experience:

Familiarity with

Data Lakehouse

architectures, particularly with tools like

Delta Lake

,

OneLake

, etc.

Azure Ecosystem Familiarity:

Knowledge of

Azure's full ecosystem

for end-to-end data integration and ETL processes.

Proficiency in PySpark and Python:

Expertise in

PySpark

for data processing tasks, with a solid foundation in

Python

.

Fabric Integration:

Familiarity with

Fabric

and how it integrates with other services within the

Azure ecosystem

. *

Databricks Experience:

Experience with

Databricks

is a plus.