Atakan Tuncer

● Build and maintain large-scale data platforms on Azure Databricks. ● Architect reliable, scalable data solutions. ● Develop and deploy ML solutions ● Deploy AI agents for different departments of the organization

* Developing Airflow DAGs to build and manage data pipelines for various customers, utilizing Azure and AWS cloud environments. * The development and production environments for pipelines are built within Databricks, ensuring data is efficiently prepared and transformed for AI models. * Implementing CI/CD processes using GitHub Actions and Jenkins to automate the deployment of data pipelines. * Collaborating with cross-functional teams to understand data requirements and deliver efficient and scalable data solutions. * Migrating a Couchbase database to various AWS databases, including DynamoDB, DocumentDB, and Snowflake. * Using Terraform, Python, and SQL to perform the migration and implement infrastructure as code. * Designing and implementing data models to ensure data integrity and accessibility in the new environment * Workflow automation via N8N for a ai powered chatbot * Built ai agents with openai api using assistants and function calling for automating text extraction, contract parsing

Skills: Veri Mühendisliği · Python · Veri Mimarisi · Data Build Tool (DBT) · Apache Spark · Snowflake

Mentoring at the Data Engineering with Azure in Udacity

- Building and managing targeted web scrapers, including but not limited to ad-hoc scraping tasks and production level regularly recurring scraping jobs. - Managing the pipeline and storage for the data of those scrapers. - Containerizing/Deploying the crawlers to Cloud - Monitoring crawling process - Working closely with Data Scientists and Product Team to build and develop future data pipelines as defined by the business. - Version tracking with Git - Scrum meetings and planning

• Creating scripts for web scraping with Scrapy and Selenium (scraped over 500 websites from different domains) • Analyzing customer requirements and propose solutions to corporate and individual customers • Carry out quality assurance tests to discover errors and optimize usability • Creating scripts for automating tasks like network monitoring, etc • Distributing tasks to freelancers and reviewing pull requests via Jira • Creating ETLs and integrating them with pipelines

- Played a vital role implementing new data analytics platform as a domain owner in data modelling and designing pipelines - Utilized data warehousing programs for data intelligence - Data acquisition from different sources such as APIs, cleansing and saving them to data repos with Python - Creating ad-hoc reports and optimizing existing queries with SQL - Requirement specifications on data needs for managing external production process

Analyzing in-house and vendors production and delivery data until the final client Convert data into actionable insights and forecasting future needs for production and delivery Creating reports for helping data driven business decisions Ad-hoc analysis on production, delivery and quality data Automating reporting with VBA – Excel