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Strong employer brand, popular senior data role, and metro locations increase candidate competition.
PySpark and cloud data engineering skills are broadly transferable across industries.
Explicit 7–10 years plus mandatory PySpark, ADF, and Azure skills create highly strict shortlisting.
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Design, develop, and maintain end-to-end scalable data pipelines using PySpark for large-scale batch and incremental data processing.
Build and orchestrate data ingestion and transformation workflows using Azure Data Factory while optimizing Spark jobs for performance and cost efficiency.
Collaborate with data architects to implement data models and ensure data quality, validation, and reconciliation across source and target systems on Azure cloud platforms.
7–10 years of experience in data engineering or related roles.
Strong hands-on expertise in PySpark, Azure Data Factory (ADF), and Azure Synapse.
Experience with Azure cloud ecosystem including Azure Data Lake Storage (ADLS) Gen2, SQL, and Spark SQL.
Solid understanding of ETL/ELT concepts, data warehousing, data lakes, and data partitioning/file formats (Parquet, ORC, Avro).
Deep experience operating in cloud-based data engineering environments, specifically Azure, handling complex data pipelines and performance optimization.
Proven ability to collaborate cross-functionally with data architects and analytics teams to deliver reliable and scalable data solutions.
Strong technical proficiency in building reusable data frameworks and enforcing data quality and best practices in large-scale data processing.