Description

You will build and maintain scalable data pipelines and data lakes on AWS, focusing on performance, governance, and reliability.

This role is hybrid.

Responsibilities

• Design and optimize Apache Iceberg tables, managing schema evolution and performance tuning.
• Build and deploy scalable ETL/ELT pipelines and real-time streaming architectures using Spark, Flink, or PySpark.
• Develop and automate data workflows in Python, including APIs and infrastructure scripts.
• Collaborate on system design, establish data governance best practices, and ensure monitoring and reliability.
• Write complex SQL and apply strong data modeling principles to support analytics needs.

Required Skills

• 4–10+ years of data engineering experience.
• Deep hands-on expertise with Apache Iceberg (table design, schema evolution, optimization).
• Strong experience with AWS data stack: S3, EMR, Lambda, Glue, IAM, Step Functions, CloudWatch.
• Proficiency in Python for data pipelines, automation, and APIs.
• Experience with distributed processing frameworks: Spark, Flink, or PySpark.
• Expertise in building and deploying data warehouses/lakes.
• Strong SQL and data modeling skills.

Preferred Skills

• Experience with Kafka for real-time streaming.

Key Skills

Education

Bachelor's degree