Data Engineer II

Responsibilities

• Build and optimize scalable data pipelines and lakehouse components using Iceberg or Hudi.
• Develop ETL/ELT workflows on AWS using Glue, EMR, Lambda, Redshift, and other platform services.
• Write clean, modular, reusable code using PySpark, Python, and SQL.
• Manage and enhance orchestration workflows with Airflow to ensure reliability and scalability.
• Collaborate with analytics, product, and engineering teams to maintain unified and consistent data models.
• Participate in performance tuning, cost optimization, and improvement of AWS data infrastructure.
• Implement and follow best practices in data quality, cataloging, and metadata management.
• Contribute to code reviews and engineering discussions to maintain high technical standards.
  

Requirements

• 2-4 years of experience in data engineering with strong exposure to large-scale data systems.
• 2+ years of hands-on experience in PySpark.
• Solid understanding of the AWS data ecosystem: Glue, EMR, S3 Lambda, Redshift, CloudWatch.
• Practical experience working with Apache Iceberg or Hudi(Iceberg preferred).
• Strong programming skills in Python, PySpark, and a solid command of SQL.
• Experience working with Airflow for scheduling and orchestration.
• Understanding of distributed systems, data modeling, and data governance principles.
• Exposure to containerized environments (Kubernetes, Docker)is a plus.
• Ability to work closely with business and technical teams to deliver scalable solutions.