Data Analytics & Visualization

Leveraging powerful AWS services like Amazon S3, AWS Glue, and Amazon Kinesis, Amazon Athena, Amazon DynamoDB to ingest, process, and prepare your data for analysis.
Explore AWS QuickSight s and Tableau advanced visualizations and dashboarding capabilities to bring your data to life and enable data-driven decision-making.
Demonstrate the ability to design and implement efficient data ingestion pipelines using Python or PySpark , optimizing the pipeline for performance and reliability.
Contribute to the development of a centralized Cloud Data Hub and AI Platform, leveraging AWS services to enable seamless data management.
Opportunity to work with Terraform, GIT and GIT action to Design and Implement CI/CD Pipeline for streamlined development deployment.
Engage with the AWS team to stay up to date with the latest AWS services, best practices, and industry trends.

What should you bring along:
7+ years of overall experience in data engineering, data analytics, and cloud computing.
Minimum 3-5 years of hands-on experience with AWS data services, including Amazon S3, AWS Glue, Amazon Kinesis, Amazon Athena, and Amazon DynamoDB.
Proficient in working with PySpark for large-scale data processing and analytics.
Understanding of cloud-based data architecture and the design of centralized data hubs.
Ability to integrate various AWS services to enable seamless data workflows and machine learning capabilities.
Familiarity with infrastructure as code (IaC) tools like Terraform and CI/CD principles added advantage.
Experience in advanced data visualization and dashboarding using AWS Quick Sight and Tableau.
Excellent communication and problem-solving skills, with the ability to work in a team environment.
Strong collaboration skills and experience working closely with other teams.