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Tier-1 brand, popular Data Engineer title, and Bangalore metro location elevate applicant competition.
Core Databricks/PySpark data engineering skills are transferable, but asset-management preference raises sensitivity to medium.
Mandates Databricks, PySpark, Iceberg, cloud and platform governance, enforcing strict technical filters.
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Design, develop, and maintain scalable data ingestion and transformation pipelines using PySpark and SQL on Databricks platform.
Architect and manage modern Lakehouse solutions with Apache Iceberg, ensuring performance optimization and interoperability.
Implement data quality, pipeline orchestration, monitoring, and CI/CD processes to support high-volume enterprise datasets and governance compliance.
Strong experience with Databricks, PySpark, ETL/ELT pipeline design, data ingestion, and orchestration frameworks.
Experience with Apache Iceberg or similar open table formats and cloud platforms (AWS preferred).
Mandatory skills include SQL, distributed data processing, data quality frameworks, and job orchestration tools.
Work Experience Required: Not explicitly mentioned in the JD.
Proven expertise in building and optimizing large-scale data platforms, especially on Databricks and Spark environments.
Familiarity with asset management or financial data domain is a strong plus for translating business requirements to technical solutions.
Experience implementing governance best practices including Unity Catalog, RBAC, and metadata management in enterprise settings.