We are seeking a highly skilled Senior Data Engineer to design, build, and maintain scalable data pipelines for enterprise-grade data platforms within the Risk & Compliance domain. The ideal candidate will have strong expertise in PySpark, Python, and data engineering best practices, with a focus on data quality, governance, and security.
Key Responsibilities-
Design, develop, and optimize scalable data pipelines using PySpark and Python
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Build robust ETL/ELT workflows to process large volumes of structured and unstructured data
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Collaborate with data scientists, analysts, and business stakeholders to deliver high-quality datasets
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Ensure data integrity, accuracy, and reliability through validation frameworks and monitoring
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Implement data security and access control mechanisms aligned with compliance standards
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Work closely with Risk & Compliance teams to support regulatory and reporting requirements
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Optimize performance of data processing jobs and queries
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Maintain and enhance existing data architecture and pipelines
Required Skills & Experience-
6+ years of experience in Data Engineering
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Strong hands-on experience with PySpark and Python
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Solid experience with SQL and Oracle databases
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Experience in building and maintaining large-scale data pipelines
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Good understanding of data warehousing concepts and ETL frameworks
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Experience with data validation, data quality, and governance frameworks
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Familiarity with cloud platforms (AWS/Azure/GCP) is a plus
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Exposure to banking, financial services, or risk & compliance domain is preferred
Key Competencies-
Strong problem-solving and analytical skills
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Ability to work in a fast-paced, collaborative environment
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Excellent communication and stakeholder management skills
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Attention to detail with a focus on data quality and security
Nice to Have-
Experience with Big Data ecosystems (Hadoop, Spark)
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Knowledge of data security and regulatory compliance frameworks
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Prior experience working with enterprise data platforms
