Ozan possesses extensive experience in Python development, having utilized the language for various cloud-based projects throughout his career. He has demonstrated proficiency in utilizing Python to automate tasks, develop backend services, and integrate various cloud services seamlessly. Ozan's Python expertise extends to leveraging frameworks such as Flask and Django to build robust and scalable web applications in cloud environments. His familiarity with Python libraries and tools enables him to efficiently solve complex problems and streamline development workflows. Additionally, Ozan stays abreast of the latest advancements in Python and regularly incorporates best practices and emerging technologies into his projects to deliver high-quality solutions.
Led tasks to refactor the legacy codebase using enterprise design patterns and implemented Sentry integration for performance monitoring.
Created automated ETL with AWS Step Functions, AWS SAM, AWS Lambda, S3, Python, Boto3 (each data transformation step was orchestrated by AWS Step Functions, and the data was stored in AWS S3).
Managed and addressed bottlenecks and built CI/CD pipelines with Jenkins to automate versioning and deployments.
Built and implemented data analytics reports to find irregular transactions using Pandas, Python, and Statistics.
Built data ETL jobs with PySpark and Python based on the business use case, data size, and complexity to speed up and minimize the cost by Boto3, AWS RDS, Glue Crawler, Glue Studio, S3, Athena, AWS Lambda, CloudWatch, AWS SageMaker Notebooks, and Docker.
Developed automated ETL with the orchestration of AWS Step Functions including AWS S3, Boto3, Python, AWS Lambda, and AWS SAM.
Implemented IaC to deploy the data pipeline architecture via CI/CD by CloudFormation and AWS CLI.
Created archiving via AWS Glue, S3 versioning, S3 storage lifecycle, Python, PySpark, Boto3, CloudFormation, GitHub, and Lambda to minimize RDS storage cost and secure backups.
Delivered reports and RESTful services for a property management business composed of multiple channels using Python Django, Django REST Framework and deployed the solution to AWS with AWS EC2, VPC, RDS (PostgreSQL), SQL Alchemy, and Docker.
Created a SaaS solution for Twitter integration and deployed it as serverless AWS Lambda, API Gateway, OAuth2, OAuth1, Python Flask, REST, and PyTest.
Deployed microservices for Computer Vision on Azure Kubernetes Service, configured the pods based on load tests with best numbers, created horizontal scaling based on CPU load, and implemented live and readiness probes to ensure pods are running healthy.
Worked on multinational client projects including Snowfall, VishamCorp, 369homes, Databox.to.
Actively participated in the implementation of the full project lifecycle for OpenPayd's payment microservices using a variety of Spring Boot stack, working on both development and support projects.
Developed innovative mechanisms to improve the integration of fintech APIs with OpenPayd's payment system, keeping track of issues and requests to ensure proper followup and closure.
Provided application production and deployment support using applicable methodologies, techniques, and tools.
Delivered a continuous monitoring tool with Java, Python, I/O bound architecture, Java Concurrency, and Statistics for 1.5M customer devices, saving STC around 80% in operational costs.
Translated project requirements and use cases into functional solutions, ensuring the best possible performance, quality, and responsiveness of STC's monitoring tool.
Evaluated and selected from existing and emerging technologies the appropriate tech stack options that fit STC's business needs.
Built a data cleansing tool with Java Concurrency for a client, cleaning up to 1.8M data points on the project and saving approximately £1.5M in license renewal fees.
Trained a team of 4 junior admins, managed vendor relations, and benchmarked project outcomes against client requirements.
Collaborated with the support team and presented new deployment procedures that minimized outages on the solution by 90%.
Designed Java-based solutions for Eteration clients, following client specifications, using appropriate tools/frameworks, and adhering to best coding standards.
Identified areas of instability and deviations from best practices and provided guidance and recommendations on software design and development best practices.
Identified bottlenecks and defects and devised solutions to mitigate and address issues.
Worked on a SaaS solution that transformed monolith architectures into microservices with 12-factor pattern techniques. The initial project was implemented by Data Scientists and Python developers for face detection using the relevant tech stack. Designed the new microservices architecture, handled integrations with REST APIs, and implemented containerized solutions for deployments using Docker Composer. Fixed memory leaks and decreased the complexity of services on the solution. The project is still ongoing with its underlining infrastructure becoming the standard for the rest of the microservices to be converted.
Developed new solutions to refactor the legacy codebase by introducing enterprise design patterns. Implemented Sentry integration for exception and performance monitoring to address bottlenecks and alert operations for customer satisfaction. Built a CI/CD pipeline with Jenkins for automating versioning and deployments.
Implemented AWS-based solutions on an IoT project, developing Alexa skills integration within AWS Cognito, AWS SAM, and AWS Lambda. Built REST APIs with Python Async IO to manage the microcontrollers on the IoT devices. Fixed several issues and sped up the serverless API development, applying AWS best practices.
Supported the data analyst to build Power BI screens with tables. Extracted data from the Azure SQL database, transformed the data into fact tables on the database, and presented the results via Azure Jupyter Notebook. Improved inferences on customer behaviors and more significant features to highlight reliable city groups. The project is ongoing.
Extracted data from AWS S3, normalized the data, extracted new features and applied Machine Learning, and presented results via a 2D visual diagram. Achievements include finding a premium customer segment that had higher monetary. Reduced the dataset size, irrelevant data, and the outlier was dropped from the dataset.
Built and deployed a cost-efficient microservice architecture on the REST API solution between front-end and back-end tasks, applying security best practices using AWS Cognito and AWS Secret Manager. Provided operational excellence within CI/CD tasks for the delivery of seamless deployments on the project.
Education
AWS Certified DevOps Engineer - Professional (Expires Jan 2027)
Amazon Web Services
2023 - 2024 (1 year)
AWS Certified Solutions Architect – Associate (Expired Jan 2024)
Amazon Web Services
2021 - 2021
AWS Certified Developer – Associate (Expired Dec 2023)
Amazon Web Services
2020 - 2020
Taming Big Data with Apache Spark and Python (hands-on)
Udemy
2019 - 2019
Natural Language Processing Fundamentals in Python
DataCamp
2019 - 2019
Introduction to PySpark
DataCamp
2019 - 2019
Machine Learning
Coursera
2018 - 2018
Data Scientist
DataCamp
2018 - 2018
Google Cloud Platform Essential Training
LinkedIn
2018 - 2018
MSc Business Analytics
Warwick Business School
2017 - 2018 (1 year)
Professional Scrum Master I
Scrum.org
2017 - 2017
Oracle Certified Professional, Java SE 6 Programmer