Emre Talay

Constructed the design and delivery of business scenarios within a large-scale Big Data environment, covering the full lifecycle from development and integration testing to production deployment. Ensured functional validation in close collaboration with Business Analysts, aligning technical implementations with business requirements. Operated in a highly transversal context, coordinating with multiple squads across France on key banking topics such as transfers, direct debits, and retrocessions. Led the integration of new scenarios on an on-premises Big Data platform, handling very large data volumes (up to 12 billion records per run). Oversaw deployment and release activities, while facilitating clear communication with non-technical stakeholders to ensure effective tracking of technical topics, task planning, and overall delivery alignment.

Managed and contributed to multiple end-to-end data and AI projects across diverse industries including automotive (Nissan, Renault), consumer goods (Nestlé), public transport, and luxury alcohol. Delivered scalable solutions using modern cloud platforms (Azure, GCP,Amazon), tools as (Databricks, Snowflake) and many other data engineering tools, with strong focus on au- tomation, performance, and business impact. Key contributions include : -API and Integration Development: Built data-sharing APIs in Python on Azure for dealer health check operations (Nissan), and automated data in- gestion pipelines from SFTP to SQL Server. -Cloud Data Engineering: Designed and maintained robust data pipelines using BigQuery, Databricks, Snowflake, and DBT. Used Terraform and Docker to deploy components, with CI/CD via GitHub. -Data Mesh and Monitoring: Implemented Data Mesh architecture on GCP for decentralized data processing and monitoring sales data across products using orchestration tools and dashboards (Tableau). -AI and NLP Solutions: Developed a domain-specific AI chatbot capable of answering scientific queries based on structured document inputs. -Project and Product Leadership: Acted as Project Manager and Product Owner for cross-functional teams (up to 5 members), managed timelines, QA testing, stakeholder communication, and product feedback cycles (Renault, internal tools). -BI Automation: Automated manual data workflows to enable scalable Power BI reporting for key business functions. Stack Technical:Python, SQL, DBT, Pandas. Cloud: Azure ecosystem (Data Factory, Fabric, Databricks, SQL, Storage, Containers), GCP ecosystem (BigQuery, Data Mesh, orchestration), Snowflake,Docker, Terraform, GitHub CI/CD.

Led the real-time data processing efforts for television data of 7 million users, ensuring data accuracy and availability, developed and delivered KPIs to the marketing team, enabling data-driven business decisions and improved client propositions, designed and implemented client-facing features within the Or- ange TV project interface, enhancing user engagement and revenue oppor- tunities, successfully integrated data from multiple sources to create a unified and comprehensive dataset, collaborated with cross-functional teams to align data analysis efforts with business goals, resulting in increased client satis- faction and revenue growth. Stack Technical:Apache Hadoop, Java, Spark, Pig,MapReduce, Hive, Kafka, Cron, Linux Shel

-Realization of innovative prototypes around existing solutions using Jupyter Notebook -Additional data visualization with libraries D3, matplotlib, nvd3, D3plus, Geomap,Vega, etc.. -Improved functionality with filter system, simultaneously query processing, asynchronous / parallel database call ( Request-Response options via Python on Jupyter Notebook ) Technologies: Client Side : Javascript, HTML5, CSS, Angular 5, Dojo Toolkit, Jquery Server Side : Python 3, Jupyter Notebook Database Administration and Searching Engine : MySQl, Apache SOLR Virtualization Technologies : Docker