Design, build, and optimize end-to-end ETL/ELT pipelines using Python and PySpark for large structured and unstructured datasets. Ensure data quality through validation and reconciliation, troubleshoot performance issues, implement CI/CD and code review practices, and collaborate with IT and business stakeholders to translate requirements into scalable data solutions.
Responsibilities: • Development and Maintain Data Pipelines: Design, implement, and optimize end-to-end ETL/ELT pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data. • Utilize Python and Pyspark: Write efficient, scalable and maintainable code in Python and leverage Pyspark for large-scale data processing in distributed computing environments. Also be able to review existing code and identify areas of improvement. • Ensure Data Quality and Integrity: Implement data validation, cleansing, transformation and reconciliation processes to ensure data accuracy and consistency throughout the data lifecycle. • Collaborate with Stakeholders: Work closely with IT teams and business stakeholders to gather data requirements and translate them to technical solutions. • Troubleshoot and Optimize: Monitor job performance, troubleshoot complex data issues and fine-tune for performance and scalability. • Adhere to Best Practices: Participate in code reviews, establish coding standards, and implement CI/CD pipelines for automated testing and deployment. Skills: • Strong hands-on coding proficiency in Python, Pyspark and SQL (Microsoft SQL Server preferred) • Experience with big data frameworks (Hadoop, Spark). • Experience with cloud platforms ( AWS, Azure or GCP) • Experience with Code versioning tools ( Bitbucket, Github ) • Experience with CI/CD and setting up pipelines. • Solid understanding of database design principles, data modelling, schemas and data warehousing solutions. • Excellent problem-solving and analytical skills to troubleshoot complex data issues independently.
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