Design, develop, and maintain scalable data pipelines using Databricks, PySpark, and Python
Build and optimize ETL/ELT processes for large-scale data ingestion and transformation
Develop and maintain data models, data marts, and enterprise Data Warehouse (DWH) solutions
Write efficient, complex SQL queries for data extraction, transformation, and reporting
Ensure data quality, integrity, and consistency across multiple source systems
Implement performance tuning and optimization strategies for Databricks workloads
Follow DevOps best practices for CI/CD, version control, and automated deployment processes
Collaborate closely with cross-functional teams to troubleshoot and resolve data-related issues

Required Skill sPrimary Skillse

tAtleast 3- 5 years in working with Databrick
sProficiency in Python and PySpar
kAdvanced knowledge of SQ
LStrong experience in Data Warehousing (DWH) concepts and implementatio
nExperience designing and optimizing ETL/ELT pipeline
sKnowledge of data modeling techniques (Star Schema, Snowflake Schema, Dimensional Modeling
)Experience handling large datasets, with a focus on performance optimizatio
nExperience with DevOps tools and practices (CI/CD pipelines, Git, Azure DevOps, Jenkins, etc.
)Understanding of cloud-based data platforms (Azure, AWS, or GCP
)Deep understanding of IPPL (Integrated Planning Product Line

)

Good to Ha

veExposure to SAP BDC (Batch Data Communication) processes and SAP data integration concep
tsExperience integrating SAP and non-SAP syste
msKnowledge of Delta Lake, Unity Catalog, and Databricks Lakehouse architectu

Data Engineer (DataBricks)

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