After working on several engineering projects that incorporated data-driven software tools, I determined to sharpen my software abilities to put my passion for the software and data industries to practical use. Along with this, I am a recent Seneca College Computer Science graduate. Throughout my studies, I have worked on a variety of projects in object-oriented programming, database management, and web development. Involved in and handled many team initiatives that aided in the development of various teamwork roles and leadership skills. Working as a software and data engineer or analyst position piqued my interest. I'm presently employed as a data engineer associate at the Jarvis Consulting Group, where I develop software applications using Linux, Java, and data communication tools while adhering to the Agile/Scrum techniques.
Proficient: Linux/Bash, RDBMS/SQL, HQL, Agile/Scrum, Git/Git Flow
Competent: Java, C/C++, HTML, Docker, MongoDB, DBeaver, Data visualization
Familiar: Python, Pandas, Numpy, R, Hadoop/Spark, Hive, YARN, Zeppelin, Power BI, Cloud Computing, Java Script
Project source code: https://github.com/jarviscanada/jarvis_data_eng_GozdeArslan
Cluster Monitor [GitHub]: Designed a Linux cluster hardware monitoring agent to screen server host statistics and data usage. Implemented project using various technologies such as CentOS, Bash/Shell, Docker, RDBMS/SQL/PostgreSQL, Crontab, and Git/Git Flow. Bash commands were used to collect host utilization data, which was processed using Crontab. The monitoring agent servers will run within a CentOS 7 environment. The server will save host data (hardware specifications and resource use data) into the (RDBMS) Postgres Database, which is deployed through Docker. Completed implementation on the IntelliJ IDEA which is one of the integrated developer environments. Managed and deployed the project by using Git/Git Flow.
Core Java Apps [GitHub]:
- Twitter App: Developed a Java Twitter application that allows users to create, read, and delete tweets by using Twitter REST APIs. The MVC (Model, View, Controller) design model was used to implement the application. Additionally, mainstream technologies such as Java and IntelliJ IDEA were used to build the application. The Dependency Management (DM) was implemented using the Spring framework, which was integrated with the Maven package management framework. The application was deployed using Docker Components and then uploaded to Docker Hub. JUnit and Mockito were ultimately utilized to complete the testing process.
- JDBC App: Developed a Java Database Connection(JDBC) application that enables a connection between a Java program and a relational database management system (RDBMS). Implementation was completed by using Java 8, RDBMS(PostgreSQL), Maven, Docker, IntelliJ IDEA, and Git/Git Flow. The application utilizes data access objects(DAOs) and Repository Architectural Patterns also certain advanced JDBC principles. Implemented CRUD operations for multiple DAOs that tested on the SQL queries. Maven build automation tools were used to handle the project. Testing and debugging were performed on the IntelliJ IDEA.
- Grep App: Reimplemented an application to search matching patterns within the files by using grep commands. Developed the application using Java 8, Maven, Docker, IntelliJ IDEA, and Git/Git Flow. Application package tools managed by the Maven-based on the project object model. Docker handled deploying the application via Docker Hub. Using Java 8 Stream APIs and Lambdas were assisted in effectively managing the usage of memory. Testing and debugging were accomplished on the IntelliJ IDEA. Versions and collaborations controlled using Git/Git Flow.
Python Data Analytics [GitHub]: Currently working on project.
Hadoop [GitHub]: Solved business problems by using Core Hadoop components such as HDFS, MapReduce, and YARN, which process large amounts of data provided by an analytics team. This Hadoop project will assist us in evaluating the Hadoop ecosystem and how it interacts with a distributed system for data storage and processing. Apache Hive and Zeppelin Notebook were used to handle the business problem. The Google Cloud Platform's data proc function enables the Hadoop cluster to use several machines to manage dataset, which utilizes one master node and two worker nodes in the cluster and uses Hive. Project versions are controlled by Git/Git Flow.
Web App for Movie Streaming [GitHub]: Designed a movie streaming website with features such as movie listings, searching, registration and sign-in, and purchase. Implemented the application using JavaScript, Node.js, Express-Handlebars, CSS, Visual Studio Code, MongoDB, and Heroku. The application uses the MVC(model-view-controller) design pattern that specifies an application consists of a data model, presentation information, and control information. Node.js and express libraries are used to handle the Back-End applications. CSS is used to govern the front-end design. Database managed utilized on MongoDB. Application deployed through Heroku by using GitHub connection.
A Retail Application [GitHub]: Implemented a retail application that allows a user to add products to a cart for a specific consumer and estimates the overall cost of all the items. The order information is stored in the Oracle database after checkout. The project was developed by using the Oracle, PL/SQL, and C++ programming languages.
Data Engineer Associate, Jarvis Consulting Group (2021 - present): Develop, test, and maintain various software applications and architectures as Data Engineer. Improve experience with Java, SQL, Linux, PostgreSQL, Docker, and Maven by applying the SDLC best practices. Use Git and Git Flow to manage the versions of each project effectively. Collaborate with developer team by an Agile/Scrum Framework. As a team leader, organize daily scrum meetings and sprint retrospective meetings. Along with this, I am improving my aptitudes in working on real-world projects, prioritizing the work tasks, and organizing by applying analytical thinking principles.
Project Data and Method Engineer, Jantsa Wheel Industry (2016 - 2018): Analyzed product data to create a new automation system project associated with production requirements. Using SQL and data management tools over 1500 products data were performed ETL process on the ERP software program. Excel macros, tables, and data visulazation tools were used to manage deviation and data regulations. Data components were used, product time measurements were taken, and statistical process control analysis was performed (SPC). On the based products, 70% of data variances were eliminated. Organized weekly update meetings during the project. Results and evaluations were presented to the project team as well as the managers, and an executive summary describing the values were written as required. Productivity was increased by 20%.
Seneca College (2020-2021), Diploma of Applied Sciences, Computer Programming
- Scholarship
- Dean's List (Summer 2020): GPA : 4.0/4.0
- GPA: 3.7/4.0
Kutahya Dumlupinar University (2010-2014), Bachelor degree in Engineering, Industrial Engineering
- Problem Solving Techniques certificate (2017)
- Teamwork training certificate (2017)
- Complete Python Bootcamp (Udemy)
- Data Visualization for Data Analysis (Linkedin Learning)
- Volunteering, organization helper member - Dundas West Festival (2018)
- Reading Novel (Sci-Fi)
- Watching intellectual Youtube channels and TED Talks
- Games: Chess, Table tennis
- Yoga