Design and implement scalable data pipelines using AWS services like S3, Glue, and Lambda to ingest, process, and store large datasets.

Develop and maintain data models and schemas in AWS data stores such as Redshift, DynamoDB, and RDS, ensuring data integrity and performance.

Implement data governance and security measures, including access controls, encryption, and data masking, to comply with regulatory requirements and organizational policies.

Optimize data processing and storage solutions for performance and cost-efficiency, leveraging AWS cost management tools and best practices.

Monitor and troubleshoot data pipeline performance, identifying and resolving bottlenecks and data quality issues using AWS monitoring tools like CloudWatch.

Collaborate with data scientists and analysts to understand their data requirements and provide them with the necessary data infrastructure and tools.

Automate data engineering tasks using infrastructure-as-code (IaC) principles with tools like AWS CloudFormation or Terraform.

Build and maintain data catalogs and metadata management systems to ensure data discoverability and lineage.

Implement data quality checks and validation rules to ensure data accuracy and consistency throughout the data lifecycle.

Stay up-to-date with the latest AWS data engineering services and best practices, and evaluate their applicability to the organization's needs.

Participate in data architecture design and planning, contributing to the overall data strategy of the organization.

Develop and maintain documentation for data pipelines, data models, and data engineering processes.

Architect scalable data pipelines using AWS services like Glue, Kinesis, and Lambda for efficient data ingestion and processing.

Develop and maintain ETL processes to transform raw data into usable formats for analytics, leveraging AWS Data Pipeline and Step Functions.

Implement and manage data warehouses using Amazon Redshift, ensuring optimal performance and data integrity for reporting and analysis.

Administer AWS databases like RDS and DynamoDB, focusing on performance tuning, backup, recovery, and security best practices.

Design and implement data models optimized for analytical workloads, ensuring data consistency and efficient querying.

Manage and optimize AWS cloud infrastructure for data engineering tasks, including EC2, S3, and IAM configurations.

Implement security measures to protect sensitive data in AWS environments, adhering to compliance standards and best practices.

Set up monitoring and logging systems using CloudWatch to track data pipeline performance and identify potential issues.

Automate data engineering tasks using scripting languages and AWS services to improve efficiency and reduce manual effort.

Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver effective solutions.

Optimize AWS data infrastructure costs by identifying and implementing efficient resource utilization strategies.

Create and maintain documentation for data pipelines, data models, and other data engineering processes.