▪ Manage Google Cloud infrastructure using Terraform to ensure reproducibility and reliability.▪ Build an internal web application in Go (Golang) to streamline reporting and enable user file uploads, enhancing operational efficiency.▪ Develop and optimize CI/CD pipelines with Azure DevOps to improve build and deployment workflows.▪ Mentor team members and cross-functional teams on cloud-native architectures and programming best practices, fostering technical growth and collaboration.▪ Contribute to migrating existing data processes from on-premises Jupyter Notebooks and AWS to Apache Airflow in Google Cloud, improving automation and cloud-native integration.▪ Lead the design and implementation of an API using Apigee in Google Cloud Platform, enabling other technical teams to programmatically access popular datasets.Tech Stack: GCP, Azure DevOps, Python, Golang, Docker, Terraform, Git

▪ Spearhead the design and initiate the implementation of a robust data architecture.▪ Develop six data pipelines from scratch harnessing Python, Docker, and AWS services.▪ Mentor a team of four colleagues and provide guidance to two additional teams in design, coding, and AWS methodologies, fostering skill development and collaboration.▪ Offer support to cross-functional teams in resolving Python and AWS-related issues, contributing to smoother operations.▪ Streamline and optimize existing data pipelines while addressing technical debt, resulting in increased system performance and maintainability.▪ Assist in the alpha development of an LLM chatbot, leveraging internal datasets to enhance AI capabilities.▪ Orchestrate the setup of continuous integration and continuous delivery (CI/CD) processes and workflows, utilizing the AWS Cloud Development Kit (CDK), and establish best practices and guidelines for efficient and reliable software deployment.Tech Stack: AWS, Python, Docker, Typescript, SQL, Git

▪ Established and oversaw continuous integration and continuous deployment (CI/CD) processes using Azure DevOps, ensuring seamless software development and deployment.▪ Crafted and sustained personalized SQL queries to extract, manipulate, and analyze data, contributing to data-driven decision-making.▪ Conceptualized, executed, and maintained Python-based data pipelines to seamlessly integrate internal and third-party data sources, enhancing data accessibility and analysis.▪ Spearheaded the development and implementation of extract, transform, load (ETL) processes via Databricks and Python, enabling efficient data transformation and loading operations to a Snowflake data warehouse.▪ Assumed a role in the quality assurance process by meticulously reviewing pull requests and conducting comprehensive code reviews, ensuring code quality and adherence to best practices.Tech Stack: Azure, Python, Databricks, Snowflake, SQL, Git

▪ Provided Linux system implementation, offering support, proactive monitoring, and maintenance to ensure optimal system performance and reliability.▪ Conducted daily system operations, verifying system integrity and availability. This involved continuous performance tuning, executing hardware and OS upgrades, and optimizing system resources for peak efficiency.▪ Implemented robust monitoring systems for both applications and servers, demonstrating a swift response to alarms and maintaining high availability and performance.▪ Managed the deployment of both physical and virtual Linux servers, executing assigned incident management tasks with precision. Delivered tier III support in response to service requests, ensuring rapid issue resolution and maintaining uninterrupted service delivery.Tech Stack: RHEL, Bash, Python