ETL DataStage Developer (Financial Services / Insurance)

Overview

We are seeking an experienced ETL DataStage Developer to design, develop, and maintain enterprise data integration solutions within the Financial Services / Insurance domain. The ideal candidate will have strong expertise in IBM InfoSphere DataStage, data warehousing concepts, and ETL development, along with experience handling large-scale financial data and regulatory reporting requirements.

This role will work closely with data architects, business analysts, and application teams to build scalable data pipelines that support analytics, reporting, and operational systems.

Key Responsibilities

• Design, develop, and maintain ETL workflows using IBM InfoSphere DataStage.
• Develop and optimize ETL jobs for large-scale data processing from multiple source systems.
• Build and maintain data pipelines for data warehouse and reporting systems.
• Perform data extraction, transformation, and loading (ETL) from structured and semi-structured data sources.
• Implement data quality checks, validation rules, and error handling within ETL processes.
• Optimize ETL processes for performance, scalability, and reliability.
• Collaborate with business analysts and data architects to understand data requirements and translate them into technical ETL solutions.
• Work with financial and insurance data models including policy, claims, underwriting, and customer data.
• Troubleshoot and resolve ETL failures, performance issues, and data inconsistencies.
• Ensure compliance with data governance, security, and regulatory standards.
• Maintain technical documentation, ETL design specifications, and data flow diagrams.

Required Skills & Experience

• 5+ years of experience in ETL development using IBM DataStage.
• Strong experience with IBM InfoSphere DataStage Designer, Director, and Administrator.
• Solid understanding of data warehousing concepts, ETL architecture, and data modeling.
• Experience with SQL and relational databases (Oracle, SQL Server, DB2, or similar).
• Hands-on experience in data transformation, data validation, and performance tuning.
• Experience working with large datasets and enterprise data integration projects.
• Strong knowledge of Unix/Linux scripting.
• Experience with version control tools such as Git or SVN.
• Strong analytical and problem-solving skills.