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Tier-1 brand, metro location, and common data-engineer title increase applicant competition.
Data engineering skills are broadly transferable, though asset-management preference moderately limits fit.
Strong mandatory Databricks, PySpark, Iceberg, and data platform experience increases shortlisting rigor.
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Design and build scalable ETL/ELT data pipelines and modern Lakehouse architecture on Databricks using PySpark and SQL.
Implement and manage data ingestion, transformation, quality checks, pipeline orchestration, and job scheduling for high-volume enterprise datasets.
Optimize Spark jobs for performance and cost, maintain platform governance including Unity Catalog, RBAC, and collaborate with business and technical teams, with asset management domain exposure preferred.
Strong experience with Databricks, PySpark, ETL/ELT pipeline development, and Apache Iceberg/open table formats.
Proficiency in data ingestion, pipeline orchestration, SQL, and distributed data processing.
Cloud experience, preferably AWS.
Work Experience Required: Not explicitly mentioned in the JD.
Demonstrated ability to architect and optimize Lakehouse data platforms focused on performance and governance.
Experience translating business requirements into technical solutions within asset management or financial data domains.
Skilled at managing end-to-end data pipelines with CI/CD, monitoring, and orchestration tools in a large enterprise environment.