.**RH**:Raul García**Position**:AWS Data Engineer**Location**:Aguascalientes, Mexico**Industry - Sector**:MALS**What you'll do?**- Build and maintain end-to-end ETL pipelines on AWS using services like AWS Glue, AWS Lambda, Amazon Kinesis, and Amazon Redshift.- Implement batch and real-time data processing workflows to integrate data from multiple sources.- Design and implement data lakes using Amazon S3 to store structured and unstructured data.- Build and optimize data warehouses using Amazon Redshift, ensuring efficient data storage and retrieval.- Architect scalable and high-performance data solutions in the AWS Cloud using services such as AWS S3, AWS Glue, Amazon Redshift, Amazon RDS, Amazon Aurora, and Amazon DynamoDB.- Ensure that data pipelines and data storage are optimized for performance, cost efficiency, and scalability.- Design and implement data transformation logic using AWS Glue, AWS Lambda, or Apache Spark (running on AWS EMR or Databricks).- Clean, preprocess, and aggregate data to create datasets suitable for analytics and machine learning.- Automate the deployment of data pipelines and infrastructure using AWS CloudFormation, AWS CDK, and Terraform.- Implement CI/CD pipelines for data engineering workflows using AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy.- Implement data governance and security best practices, including encryption, access control using AWS IAM, and data masking.- Ensure compliance with regulatory requirements and internal policies for data security, privacy, and governance.- Continuously monitor and optimize data pipelines and data storage for performance and cost efficiency.- Troubleshoot issues related to data processing, pipeline failures, and system performance.- Collaborate with cross-functional teams (data scientists, analysts, business stakeholders) to understand data needs and provide technical solutions.- Provide mentorship and guidance to junior data engineers on best practices for AWS data engineering.- Set up monitoring and logging using AWS CloudWatch to ensure data pipelines are running smoothly.- Document data engineering processes, workflows, and solutions for internal reference and training.**What you'll bring**:- Expertise in AWS Glue, Amazon Redshift, Amazon S3, AWS Lambda, Amazon Kinesis, Amazon RDS, AWS Aurora, and AWS DynamoDB.- Proficient in building and maintaining ETL pipelines using AWS Glue, AWS Lambda, Apache Spark, or AWS Data Pipeline.- Experience in data extraction, transformation, and loading from diverse sources (relational, NoSQL, flat files, APIs, etc.).- Experience working with Amazon Redshift and Amazon S3 to build data warehouses and data lakes.- Strong understanding of data modeling techniques, dimensional modeling, and schema design for analytical purposes.- Proficiency in Python, SQL, and JavaScript for building data processing scripts, automation tasks, and custom data solutions