About the Role

We are seeking a highly skilled
Data Engineer – Microsoft Fabric (PySpark)
to design, develop, and optimize modern data engineering solutions on the Microsoft Fabric platform. The ideal candidate will have strong expertise in
PySpark, Spark SQL, Microsoft Fabric Lakehouse, Data Factory Pipelines, and ETL/ELT development
, with the ability to build scalable, high-performance data processing solutions for enterprise analytics.

Key Responsibilities
- Design, develop, and maintain scalable
data processing solutions
using
PySpark
within
Microsoft Fabric
.
- Build, optimize, and manage
ETL/ELT pipelines
for processing large-scale structured and semi-structured datasets.
- Develop high-performance data transformation workflows using
Spark DataFrames
and
Spark SQL
.
- Create and maintain robust
data ingestion, transformation, and aggregation pipelines
using
Fabric Notebooks
,
Lakehouse
, and
Data Factory Pipelines
.
- Implement best practices for data engineering, including performance tuning, partitioning, caching, and query optimization.




- Design reliable and reusable data models to support business intelligence and advanced analytics initiatives.
- Collaborate with data architects, analysts, and business stakeholders to understand data requirements and deliver scalable solutions.
- Monitor pipeline execution, troubleshoot failures, and optimize workloads for reliability and efficiency.
- Ensure data quality, governance, and security standards are followed across all data engineering processes.
- Document technical solutions, workflows, and deployment processes to support operational excellence.

Required Skills & Qualifications
- Strong hands-on experience with
Microsoft Fabric
and
PySpark
.
- Expertise in
Spark SQL
,
Spark DataFrames
, and distributed data processing.
- Experience building enterprise-grade
ETL/ELT pipelines
.
- Hands-on experience with
Fabric Notebooks
,
Lakehouse
, and
Data Factory Pipelines
.
- Robust understanding of data ingestion,

📌 Data Engineer (Chennai Metropolitan Area)
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