Data Engineers required ASAP
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Job Description
Job Title: PySpark Data Engineer Location: Experience: 5+ Years
Employment Type: Permanent (Full-Time)
Currently Serving 30 Days / Immediate to 30 Days Preferred
Our client is a trusted global innovator of IT and business services, present in 50+ countries. They specialize in digital & IT modernization, consulting, managed services, and industry-specific solutions. With a commitment to long-term success, they empower clients and society to move confidently into the digital future.
Informatica BDM (Big Data Management)
SQL
ETL / ELT Development
Big Data Technologies (Hadoop / Spark)
Data Modeling
Banking / Financial Services Domain
Bachelor’s degree in Computer Science, IT, or related field
- 5+ years of experience in Data Engineering or related roles
- Strong hands-on experience with PySpark for large-scale data processing
- Experience in ETL/ELT development and workflow orchestration
- Solid experience with Informatica BDM
- Strong knowledge of SQL and data modeling concepts
Experience with data lake and data warehouse architectures
Exposure to cloud platforms or big data ecosystems
Knowledge of data governance, security, and compliance (Banking domain)
Experience working in Agile environments
Hands-on experience with Python-based data engineering frameworks
Design, develop, and maintain scalable data pipelines using PySpark
Develop and manage ETL/ELT workflows using Informatica BDM
Collaborate with data architects, analysts, and stakeholders
Optimize data pipelines for performance and scalability
Implement data quality, validation, and governance processes
Integrate data from multiple sources (databases, APIs, enterprise systems)
Support data warehouse and data lake initiatives
Ensure compliance with banking regulatory and security standards
Troubleshoot and resolve data pipeline issues and bottlenecks
Participate in code reviews, testing, and deployment processes
Experience in Banking or Financial Services domain
Exposure to regulatory reporting systems or large-scale financial datasets
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