Description

You will design, build, and maintain scalable data pipelines and data lake infrastructure on AWS.

This role is on-site.

Responsibilities

• Design and maintain ETL/ELT pipelines using AWS Glue, Lambda, Step Functions, and EMR.
• Develop scalable ingestion solutions for structured and unstructured data sources.
• Build data transformation workflows using PySpark and Spark-based frameworks.
• Manage large-scale data lakes on S3, implementing partitioning and cataloging via Glue Data Catalog.
• Optimize Amazon Redshift data models and build high-performance ELT workloads using SQL, Spectrum, and COPY commands.

Required Skills

• 7+ years of hands-on experience in data engineering.
• Deep expertise with AWS services: S3, Glue, Redshift, Lambda, IAM, CloudWatch, and EMR.
• Strong SQL skills with experience in data modeling (star/snowflake schemas).
• Hands-on experience with PySpark/Spark or similar distributed processing frameworks.
• Experience with streaming technologies such as Kinesis or Kafka.
• Deep knowledge of Amazon Redshift optimization and performance tuning.
• Strong understanding of ETL/ELT architecture and data integration patterns.

Preferred Skills

• Experience with large-scale data lake management and optimization.
• Background in building high-performance ELT workloads.

Key Skills

Education

Any Gradute