AWS Data Engineer Resume

• AWS Glue PySpark jobs (Creating complete data ingestion)
• Delta Lake in AWS Glue and Amazon S3
• AWS Step Functions (The primary daily automation tasks for the project involve leveraging intrinsic functions and utilizing the Context object effectively)
• AWS Lambda (Calling other aws services)
• AWS Sagemaker (training model it within the context, and incorporating recommendations)
• Pulumi & Terraform Code as Infrastructure
• Amazon RDS, Amazon S3
• AWS Identity and Access Management (IAM)
• AWS Billing and Cost Management

• Leading the PoC initiative to migrate critical data processing and analysis pipelines into AWS Cloud with AWS Glue, Lake Formation and Redshift
• Managed and empowered ETL team that created the analytical database of National Database on products, packaging and waste management (BDO database)
• Collaborated and back up other teams as Database Developer with repair errors that require most careful analysis of business logic
• Conducting training for new employees. Introducing procedures and work culture in the institution and improving professional qualifications by sharing with my knowledge and experience on the case studies (I didn’t realize before that teaching is fun and gives a ton of satisfaction)

• Lead Developer for Real-Time Data Collection Systems: Leading the development of systems that collect real-time data from 3000 ticket vending machines into a PostgreSQL database.
• Web Application Development: Creating and maintaining web applications that provide functionalities such as inspection, financial reporting, and information for field service support.
• Database Management and Optimization: Ensuring efficient data storage and retrieval by managing the PostgreSQL database and optimizing it for performance to handle real-time data inputs.

• Member of the Big Data DevOps and Administrations team
• Responsible for automating Big Data Platforms configuration and management with IaaC approach based on Ansible and Docker (Cloudera, Hortonworks)
• Lifting and shifting existing offers’ portfolio into the Cloud to match the latest clients needs (mainly AWS and Azure)
• Preparing PoC around data analysis scenarios on AWS (AWS EMR and Redshift)
• Tailoring platforms components to specific clients’ needs
• Diagnosing and solving issues on production

• Linux System Support: Providing support for scientific teams by managing and maintaining Linux systems, ensuring their smooth operation and addressing any issues that arise.
• In-Depth Learning and Application of Linux: Engaging in intensive learning and application of Linux system administration skills to support the needs of the scientific research environment.
• Troubleshooting and Maintenance: Diagnosing and resolving technical issues related to Linux systems, ensuring the reliability and performance of the computing infrastructure for research activities.

• Server and Data Storage Management: Managing and maintaining servers and data storage systems to ensure their reliability, performance, and security.
• Technical Support and Troubleshooting: Providing technical support and troubleshooting for server and storage-related issues, ensuring minimal downtime and optimal performance.
• Infrastructure Setup and Configuration: Setting up and configuring computing infrastructure, including installing and configuring hardware and software components to meet organizational needs.