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Schedule Interview NowMy name is Timothy K. and I have over 7 years of experience in the tech industry. I specialize in the following technologies: node.js, JavaScript, Python, Amazon Web Services, Penetration Testing, etc.. I hold a degree in , , , , , , . Some of the notable projects I’ve worked on include: Amazon ECS CI/CD Pipeline, Fastly Access Log aggregation in AWS for FE Dashboard integration, Serverless RSS Aggregator, Lambda Layers, S3 Encryption: In-transit (KMS CMK), etc.. I am based in Albany, United States. I've successfully completed 20 projects while developing at Softaims.
I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.
I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.
I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.
Main technologies
7 years
6 Years
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Potentially possible
Amazon
Create a CI/CD pipeline. When a commit is made in the GitHub repository's "main" branch, a process is triggered that: - automatically generate tags for both the ECR image and GitHub (with semantic versioning) - build & publish a new container image in Amazon ECR - updates the Amazon ECS Task Definition - provision/deploy new containers in Amazon ECS with the updated Task Definition.
Collect and analyze data from the Content Distribution Network (CDN): - Aggregate Access Logs originating from the Fastly CDN via Amazon Kinesis Data Streams and Data Firehose - Parse logs for target HTTP headers with a AWS Lambda function - Process & store parsed data in Amazon DynamoDB - Provide AWS CLI access for the DynamoDB Table so queried data can be integrated in a Frontend Dashboard
Leveraging serverless architecture (AWS Lambda, DynamoDB, S3, SQS, and Amazon EventBridge), collect RSS feeds from specified news & podcast media platforms and aggregate them into a central store (DynamoDB)—query data from the central store to generate a single-page application hosted on a Lambda Function URL.
There was a requirement to include critical Python modules that were not part of the Python Standard Library. Lambda functions have a hard limit on deployment size at 50 MB. To work around this limitation, custom Lambda Layers were utilized by installing the large Python modules on them. This enable the Lambda function's deployment size to increase to 250 MB including the Lambda Layers. Lambda Layers can also be shared across multiple Lambda functions simultaneously.
Enable S3 encryption for data in-transit. Common use cases include managing S3 Buckets that contain sensitive data such as PII, or situations where compliance is critical (e.g. HIPAA).
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