Suada Mulgeci

Worked in cross-functional, project-based teams to design and optimize data platforms on Azure and GCP. - Built and orchestrated ETL/ELT pipelines with Databricks, ADF, and Azure Functions. - Integrated APIs, streams, and database extracts into a centralized Data Lake. - Maintained CI/CD with Terraform, Azure DevOps, and Pytest. - Developed DWH solutions on SQL Server, BigQuery, and Azure Synapse. - Supported architecture planning and automated workflows incl. token management and access control.

Took over and scaled a modern data platform on GCP and dbt Cloud. - Took ownership of the Data Warehouse and ELT pipelines (BigQuery, dbt Cloud). - Integrated new data sources via bulk exports, streaming, and services (e.g., Firebase, Cloud SQL, GA4). - Migrated orchestration (dbt jobs, Cloud Functions, Databutton scripts) to Cloud Composer (Airflow). - Set up CI/CD with GitHub Actions, Cloud Build, and dbt testing. - Supported product analytics via tailored data models and stakeholder consultation.

Led modernization of legacy ETL systems and internal data platform migration. - Took ownership of SQL Server-based DWH and ETL workflows. - Modernized legacy SSIS pipelines into ELT processes using dbt and Airflow (OTC). - Migrated the core database from MS SQL Server to Exasol. - Introduced structured project management using JIRA and Confluence, improving workflow transparency and team alignment. - Delivered backend data for real-time reporting in the company’s software product.

Contributed to interdiscip. research in machine learning theory and applied physics. - Focused on stochastic modeling, optimization, and approximation theory within a project on learning-based data analysis. - Analyzed the mathematical foundations of machine learning models for density estimation, including Generative Adversarial Networks (GANs). - Collaborated with experimental physics on the paper: “Calomplification – The Power of Generative Calorimeter Models” (Bieringer et al., 2022).

Facilitated weekly digital tutorial sessions, guiding undergraduate students through core concepts and problem sets in “Mathematical Stochastics”.

Conducted statistical analyses and data processing to improve job recommendation systems. - Analyzed large datasets using Hive to identify patterns and optimize recommendations. - Extracted relevant information from job postings with Python scripting. - Developed and validated test datasets to support model evaluation and refinement.

- Performed statistical analyses of user data using Exasol and R. - Created analytics reports for multiple business units using MicroStrategy in collaboration with the central Business Intelligence team.

Led a tutorial session for the undergraduate course “Numerical Mathematics”, guiding students through complex mathematical concepts and problem-solving techniques.

Provided interpretation services for unaccompanied minor refugees at initial care centers in Hamburg, supporting communication between refugees and social services.

Academic research at the UHH in collaboration with the KIT in the subject of mathematics focused on the statistical analysis of machine learning models for density estimation, particularly Generative Adversarial Networks (GANs).

Bachelor thesis on the Bernstein-von Mises theorem: https://kataloge.uni-hamburg.de/DB=1/SET=4/TTL=1/MAT=/NOMAT=T/CLK?IKT=1016&TRM=Bernstein-von