Data Engineer

We are looking for a skilled Junior & Senior Data Engineers with strong expertise in Python, PySpark, and SQL to design, develop, and optimize scalable ETL/data transformation pipelines. The ideal candidate should have hands-on experience working with large datasets, building efficient data workflows, and supporting data integration and analytics initiatives.

Key Responsibilities

• Design, develop, and maintain scalable ETL/data transformation pipelines using Python, PySpark, and SQL.
• Extract, transform, and load data from multiple structured and unstructured data sources.
• Develop and optimize PySpark jobs for processing large-scale datasets.
• Write efficient and complex SQL queries, stored procedures, and performance-tuned transformations.
• Perform data cleansing, validation, and quality checks to ensure data accuracy and consistency.
• Optimize data pipelines for performance, scalability, and reliability.
• Collaborate with Data Architects, Data Analysts, and Business teams to understand data requirements.
• Troubleshoot production issues and provide timely resolutions.
• Maintain technical documentation and follow coding standards and best practices.
• Participate in code reviews and continuous improvement initiatives.

Requirements

• 2–14 years of experience in Data Engineering or ETL development.
• Strong hands-on experience in Python.
• Strong expertise in PySpark for distributed data processing.
• Advanced SQL skills, including query optimization and complex joins.
• Experience in developing and maintaining ETL/data transformation pipelines.
• Good understanding of data modeling and data warehousing concepts.
• Experience handling large datasets and improving pipeline performance.
• Familiarity with Git or other version control systems.
• Strong analytical, debugging, and problem-solving skills.

Preferred Skills

• Experience with cloud platforms such as AWS, Azure, or GCP.
• Knowledge of Apache Spark, Databricks, or Hadoop ecosystem.
• Experience with workflow orchestration tools such as Apache Airflow.
• Exposure to CI/CD pipelines and DevOps practices.