Description

You will design, develop, and maintain scalable data pipelines and ETL/ELT processes across the AWS ecosystem.

This role is hybrid.

Responsibilities

• Build and optimize data pipelines using Spark (Scala/Python), AWS Glue, Lambda, and Shell scripting.
• Manage ETL/ELT workflows across S3, Hive, Apache Iceberg, and Amazon Redshift.
• Develop Redshift SQL/PLSQL code, stored procedures, and data-processing frameworks.
• Tune queries and optimize performance for structured and semi-structured data (JSON, CSV, XML, ORC, Parquet, Avro).
• Collaborate with business and technical teams to define scalable data solutions and support Customer 360 initiatives.

Required Skills

• 5+ years of experience in a lead or SME role within Data Warehouse or Big Data ecosystems.
• Strong proficiency in AWS services: S3, EC2, Redshift (RA3/Serverless), Glue, Lambda, Kafka, and Airflow.
• Expertise in Spark (Scala/Python) and Apache Iceberg.
• Deep knowledge of Redshift SQL/PLSQL and MySQL.
• Experience with data architecture, dimensional modeling, and ETL/ELT frameworks.
• Ability to manage large-scale enterprise data environments and ensure data governance.
• Strong stakeholder management and communication skills.
• Experience with data privacy regulations and supporting Data Subject Requests (DSRs).

Preferred Skills

• Experience in large enterprise or global delivery environments.
• Certifications in cloud platforms (AWS/Azure/GCP) or data engineering.

Key Skills

Education

Any Gradute