data engineer-aws

Job Description

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Kaav Inc., is seeking the following. Apply via Dice today!

Position Title: AWS Data Engineer

Location: Owings Mills, MD

Visa: H1B

Client: TCS

Experience: 13 years

Note: Need Banking or financial experience working experience in recent projects

Job Overview

We are seeking a highly motivated and skilled AWS Data Engineer to join our dynamic team. In this role, you will work with cutting-edge cloud technologies to design, implement, and manage large-scale data solutions on the AWS platform. Your expertise in data engineering, cloud architectures, and AWS services will be crucial to optimizing our data pipelines, ensuring scalability, and enabling data-driven decisions.

Key Responsibilities:

* Design and Develop Data Pipelines:
* Build and maintain scalable and efficient data pipelines using AWS services (e.g., AWS Glue, AWS Lambda, Amazon Kinesis, Amazon S3).
* Integrate and transform large datasets from various sources to facilitate data analysis.
* Data Warehousing:
* Work with Amazon Redshift and Amazon RDS to design and manage data warehouse solutions.
* Optimize performance, query efficiency, and ensure seamless data integration.
* Cloud Infrastructure:
* Collaborate with teams to implement cloud-based data solutions using Amazon EC2, Amazon S3, AWS Batch, and other AWS services.
* Leverage AWS IAM for managing user access and security.
* Data Modeling and Transformation:
* Develop data models for storage and retrieval efficiency.
* Implement and optimize ETL (Extract, Transform, Load) workflows using AWS Glue or custom solutions.
* Collaboration with Data Scientists and Analysts:
* Work closely with data scientists and business analysts to ensure data is structured and processed to meet their requirements.
* Provide support for ad-hoc queries and insights.
* Monitor and Optimize Performance:
* Monitor the performance of data pipelines and cloud infrastructure, ensuring uptime and efficiency.
* Optimize data ingestion processes for better performance and cost-effectiveness.
* Documentation and Best Practices:
* Ensure proper documentation of data pipelines, architecture, and processes.
* Adhere to industry best practices for security, data management, and cloud deployment.