Design, develop, and optimize
end-to-end ETL/ELT pipelines
using
Azure Databricks, Synapse, and Data Factory (ADF)
Implement
Medallion / Delta Lake architectures
ensuring quality and consistency of data
Build and maintain
data lake and warehouse solutions
supporting analytical and reporting needs
Optimize
Spark jobs
and pipeline performance through tuning and best practices
Develop
data models (Star/Snowflake)
and implement
SCD Type 1 & 2
for dimensional data
Automate data ingestion, transformation, and validation to improve efficiency and reduce manual work
Integrate data from multiple sources (APIs, databases, structured/unstructured files)
Collaborate with cross-functional teams across data, BI, and business units to ensure data readiness
Manage and deploy code using
Azure DevOps
and version control systems
Maintain documentation and follow SDLC best practices in agile environments

Required Skills

35 years of experience in
Data Engineering
Proficient in
Python, PySpark, SparkSQL, and Advanced SQL
Strong hands-on experience in
Azure Databricks, ADF, ADLS, Synapse
Excellent understanding of
data modeling and warehouse design
Experience in
performance tuning
and pipeline optimization
Working knowledge of
CI/CD, Git, and Azure DevOps
Good understanding of
data governance, metadata, and automation frameworks

Good to Have

Exposure to
AWS or multi-cloud environments
Knowledge of
Power BI / data visualization
integration
Experience working with
API-based data ingestion
Certifications such as
Databricks Data Engineer Associate
or
Azure Data Engineer Associate

Certifications

Databricks Data Engineer Associate
Microsoft Certified: Azure Data Engineer Associate
Azure Data Fundamentals

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