AWS Lead Data Engineer
Vijay K
Vijay is an experienced AWS Lead Data Engineer with a proven track record in designing and implementing large-scale data pipelines, analytics solutions, and cloud-based data architectures on AWS. Proficient in AWS services such as Redshift, Glue, Athena, S3, EMR, and Lambda, Vijay excels in managing and transforming data across complex environments. With strong expertise in data modeling, ETL processes, and performance optimization, Vijay ensures that data solutions are both scalable and efficient. Available for hourly, monthly, or quarterly engagements, Vijay is ready to lead your data engineering initiatives and empower data-driven decision-making across your organization.
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Responsibility
Lead the design and implementation of scalable and high-performance data architectures on AWS.
Manage and optimize AWS services like S3, Redshift, Athena, Glue, and EMR to support data processing workflows.
Collaborate with data scientists, analysts, and other stakeholders to understand business needs and provide data solutions.
Develop and implement ETL pipelines using AWS Glue, EMR, and other AWS services for data extraction, transformation, and loading.
Ensure seamless data integration from various sources into data lakes and warehouses.
Oversee the design and deployment of cloud-native data architectures with best practices in mind for scalability and security.
Develop and optimize data models and schemas in Redshift, Snowflake, and other cloud databases.
Led the migration of on-premises data systems to AWS cloud-based solutions.
Manage version control of data pipelines and processes using Git to ensure version consistency and ease of collaboration.
Optimize the performance of big data solutions using Spark and Hadoop frameworks on AWS.
Implement and maintain automated monitoring and logging systems for cloud data services.
Manage data security and compliance in AWS, ensuring proper data governance practices and privacy regulations.
Lead troubleshooting and root cause analysis for data-related issues in cloud environments.
Ensure data reliability and availability through proper backup, disaster recovery, and high-availability strategies.
Define and enforce data quality metrics and standards for all AWS-based data processing.
Implement serverless data processing solutions using AWS Lambda and other serverless technologies.
