PySpark Developer / Senior Data Engineer

We are looking for an experienced PySpark Developer with strong hands-on expertise in big data processing, distributed computing, and data engineering. The ideal candidate will have deep experience building scalable data pipelines, transforming large datasets, and working with Spark-based ecosystems in production environments.

• Design, build, and maintain scalable data pipelines using PySpark and Apache Spark
• Develop efficient ETL/ELT workflows for batch and near-real-time processing
• Optimize Spark jobs for performance, reliability, and cost efficiency
• Work with large structured and unstructured datasets
• Integrate data from multiple sources such as databases, APIs, files, and cloud storage
• Write reusable, modular, and maintainable PySpark code
• Troubleshoot job failures, data quality issues, and performance bottlenecks
• Collaborate with data architects, analysts, platform teams, and business stakeholders
• Implement data validation, monitoring, and logging frameworks
• Support deployment, scheduling, and orchestration of data pipelines
• Participate in design reviews, code reviews, and technical discussions
• Mentor junior engineers and contribute to team best practices

• Strong hands-on experience with PySpark and Apache Spark
• Deep understanding of Spark concepts such as RDDs, DataFrames, datasets, partitioning, caching, shuffling, joins, and window functions
• Strong Python programming skills
• Experience with SQL and relational databases
• Knowledge of big data concepts and distributed data processing
• Hands-on experience with ETL/ELT pipeline development
• Good understanding of performance tuning and optimization techniques in Spark
• Experience with version control tools like Git
• Familiarity with Linux/Unix environments
• Strong debugging and analytical skills