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Strong global brand, metro location, mid-level generalist data engineer role with broad in-demand skills.
Core data engineering skills are broadly transferable across industries despite financial-services preference.
Explicit 3-7 years requirement and mandatory Databricks, PySpark, cloud and pipeline expertise increases filtering.
Develop and maintain scalable batch and streaming data pipelines using PySpark, Spark SQL, and Databricks.
Design, test, and support data ingestion workflows from diverse sources such as relational databases, APIs, event streams, and cloud storage.
Monitor, troubleshoot, and optimize data pipeline performance while supporting data quality and governance initiatives.
3-7 years of experience in software engineering, data engineering, or related technical roles.
Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent experience.
Hands-on experience with Databricks ecosystem and Apache Spark concepts including transformations and performance optimization.
Proficiency in Python (PySpark), SQL, and cloud data platform operations (Azure and/or AWS).
Experience working in enterprise-scale cloud data environments with production data pipelines.
Familiar with batch, CDC, and streaming ingestion patterns and orchestration tools like Airflow or Azure Data Factory.
Exposure to financial services or asset management domains, with knowledge of governance technologies like Delta Lake or Unity Catalog.