Description

You build and maintain scalable data warehouses and lakes on AWS, focusing on Apache Iceberg table design, schema evolution, and performance optimization.

This role is hybrid.

Responsibilities

• Design and implement scalable ETL/ELT pipelines and real-time streaming architectures using Python, Spark, or Flink.
• Develop and deploy data solutions using AWS services including S3, EMR, Lambda, Glue, IAM, Step Functions, and CloudWatch.
• Establish best practices for data governance, monitoring, and system reliability while collaborating on architecture discussions.
• Optimize distributed processing frameworks and ensure robust data modeling and SQL performance.
• Automate data pipeline operations and manage infrastructure as code.

Required Skills

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

Preferred Skills

• Experience with Kafka for real-time data streaming.
• Experience with system design and cross-functional collaboration.

Key Skills

Education

Bachelor's degree