AWS Redshift Data Engineer - ETL/Python

Description :

An AWS Redshift Data Engineer designs, develops, and optimizes high-performance data pipelines and warehousing solutions. Key responsibilities include ETL/ELT development, complex SQL tuning, schema modeling, managing cluster performance (provisioned or serverless), and integrating with AWS services like S3, Glue, and Kinesis to support analytics.

Key Responsibilities :

- Data Pipeline Development : Build and maintain scalable ELT/ETL pipelines to ingest, transform, and deliver data into Redshift.

- Redshift Optimization : Monitor and tune Redshift clusters, optimize SQL queries, and manage Workload Management (WLM).

- Data Modeling : Design efficient schema, distribution keys, and sort keys for optimal analytical performance.

- Architecture & Migration : Migrate, modernize, and manage Redshift environments, including transitioning to Redshift Serverless.

- Data Integration : Utilize tools like AWS Glue, S3, Kinesis, and Lambda for data ingestion and processing.

- Collaboration & Documentation : Work with analysts to meet reporting needs and produce technical documentation.

Requirements & Skills :

- Experience : Typically 510+ years in data engineering with deep hands-on experience in Amazon Redshift.

- Technical Skills : Advanced SQL (stored procedures, views), Python for data processing, and experience with data modeling concepts.

- AWS Services : Strong knowledge of Redshift, S3, Glue, Kinesis, and IAM.

- Performance Tuning : Expertise in optimizing complex SQL and large-scale data processing.

- Preferred Certifications : AWS Certified Data Engineer Associate or Data Analytics Specialty.

Database : Amazon Redshift (Provisioned/Serverless).

ETL/Compute : AWS Glue (PySpark), Lambda, EMR.

Storage/Streaming : Amazon S3, Kinesis Data Streams.

Languages : SQL, Python.

Immediate- serving NP until month end