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Strong employer brand, metro context, and common Data Engineer title yield moderate applicant competition.
Core Databricks/PySpark data platform skills are broadly transferable; asset-management knowledge is only advantageous.
Mandated Databricks, PySpark, Iceberg, cloud, and data-platform skills create high shortlisting strictness.
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Design and develop scalable data ingestion and transformation pipelines using PySpark and SQL on Databricks platforms.
Build and maintain modern data Lakehouse architecture including Bronze/Silver/Gold layers with Apache Iceberg for enterprise-scale datasets.
Implement pipeline orchestration, data quality frameworks, and CI/CD processes for robust, performant data workflows with governance compliance.
Strong experience in Databricks and PySpark for ETL/ELT pipeline design and development.
Proficiency with Apache Iceberg or open table formats and data ingestion pipeline orchestration.
Cloud experience, preferably AWS.
Work Experience Required: Not explicitly mentioned in the JD.
Experience operating in asset management or financial data domains is a strong advantage.
Ability to translate business requirements into scalable technical solutions in complex environments.
Familiarity with data modeling concepts (Data Vault, Star Schema) and governance frameworks including Unity Catalog and RBAC.