Senior Data Engineer (Bank)

Responsibilities

• Design and develop the robust data solutions on SQL Server, ensuring optimal performance through advanced query optimization, indexing strategies, and partition management
• Design and optimize large-scale ETL pipelines using Apache Spark to process high-volume data from diverse sources across the bank
• Ensure data quality and reliability across the platform through comprehensive validation frameworks and proactive monitoring
• Collaborate with analytics teams using SAP BusinessObjects and other systems to deliver regulatory reports and business intelligence
• Drive continuous improvement by evaluating and adopting emerging technologies and best practices in big data engineering
• Troubleshoot and resolve complex data pipeline issues, performance bottlenecks, and system failures

Requirements

• Around 8 years of data engineering experience, preferably in financial services or high-volume data environments

• Advanced SQL Server expertise including:
• Query optimization, execution plan analysis, and performance tuning
• Index design, maintenance, and partitioning strategies for large tables
• Deadlock analysis, resolution, and troubleshooting
• Database design, normalization, and experience managing large-scale databases (multi-TB environments)
• Solid understanding of data engineering principles: data modeling, ETL best practices, and data quality frameworks
• Good to have the proficiency with Apache Spark (DataFrames, RDDs, Spark SQL) for building and optimizing production-grade ETL pipelines that handle large-scale distributed data processing

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