Data Engineer

We are looking for a Data Engineer with hands-on experience in Hadoop, PySpark, Python, Airflow, and AWS to build scalable data pipelines and support enterprise data platforms. The ideal candidate should be experienced in developing, optimizing, and maintaining data engineering solutions that enable analytics and data science teams.

Key Responsibilities

• Build and maintain scalable data pipelines using Hadoop, PySpark, Python, and Airflow.
• Develop ETL/ELT workflows for processing large-scale datasets.
• Collaborate with product owners, business stakeholders, and data scientists to deliver data solutions.
• Automate recurring data processes and optimize existing data engineering workflows.
• Ensure data quality, performance, and reliability across production environments.
• Participate in system design, development, testing, deployment, and ongoing support.

Mandatory Skills

• Strong hands-on experience with Hadoop, PySpark, Python, Airflow, and AWS.
• Experience building ETL/ELT pipelines and processing large datasets.
• Good knowledge of SQL and distributed data processing.
• Experience with workflow orchestration and production support.
• Understanding of data engineering best practices and Agile development.

Good to Have

• AWS services such as S3, EMR, Glue, Lambda, or Redshift.
• Spark optimization and performance tuning.
• CI/CD and Git.
• Experience working with data scientists and analytics teams.