Description

Design, build, and maintain scalable cloud-native data pipelines and distributed processing solutions.

This role is hybrid.

Responsibilities

• Architect and optimize batch and real-time data processing solutions using AWS services.
• Manage and modernize data lake and warehouse environments, ensuring high performance and security.
• Develop and maintain robust ETL/ELT pipelines for data migration and platform enhancements.
• Collaborate with analytics and business teams to deliver reliable data solutions and reporting.
• Perform performance tuning, data modeling, and database optimization across distributed systems.

Required Skills

• 10+ years of hands-on experience in Data Engineering and Data Warehousing.
• Expertise in AWS ecosystem, specifically Redshift, EMR, S3, Glue Catalog, and MWAA/Airflow.
• Strong proficiency in PySpark, Python, and SQL for complex data transformations.
• Experience building and maintaining large-scale ETL/ELT pipelines.
• Deep knowledge of data warehousing platforms including Teradata, Oracle, and Redshift.
• Hands-on experience with Apache Iceberg, data lakes, and Hive.
• Proficiency with Sqoop for data movement and Spark for distributed processing.
• Experience with Snowflake and modern data stack tools.
• Proven track record in Agile environments working with distributed teams.

Key Skills

Education

Bachelor's degree