As a skilled Data Engineer, I have utilized AWS cloud technologies and Python to design and maintain efficient data pipelines for a prominent company in the diamond industry. My extensive experience in designing and implementing scalable data pipelines has allowed me to effectively handle and process large-scale datasets. My responsibilities encompass various aspects such as sourcing data from multiple APIs and databases, performing data cleaning and transformation, and scheduling jobs through Airflow to deliver accurate business intelligence to the team. Through my expertise in Python programming, I have developed custom scripts and libraries to automate data processing tasks and ensure high data quality checks. I possess exceptional proficiency in working with renowned data processing frameworks like Spark, Pandas, and NumPy. Additionally, my proficiency in AWS services such as S3, Redshift, Glue, and Lambda has enabled me to create efficient, scalable, and fault-tolerant data processing pipelines. I have also been responsible for creating clean source data for Tableau reports that are presented to C-level managers, providing them with valuable insights into the business.

I was engaged in projects aimed at improving profit margins and productivity by executing initiatives that align with the goals and expectations of our business partners. In order to achieve these objectives, I engage in effective stakeholder communication throughout the project development phase. My work in the finance domain involves the development and implementation of artificial intelligence-based estimation methods, which enhances the operational capabilities of our business lines. Specifically, I leverage cloud-based technologies, such as Amazon Web Server, employing services such as SageMaker, Glue, and Lambda for project development and production stages.

• In this role, I held the responsibility of overseeing a team of employees to achieve organizational growth objectives. This involved conducting meetings with senior managers to determine project requirements and guiding engineers and technical staff in alignment with these requirements. Additionally, I organized Scrum meetings and ensured that project progress remained in line with the overall business plan.

• As part of my work, I contributed to several projects aimed at enhancing Unmanned Aerial Vehicle (UAV) capabilities across multiple domains. This included projects related to Machine Learning, Mathematical Modeling, Optimization, and the development of Rule-Based Systems. I played a role in all stages of these projects, from conception to production, and improved the implementation of Machine Learning algorithms by enhancing the Python language. To accomplish this, I relied heavily on data science libraries such as scikit-learn, TensorFlow, and Keras, and generally utilized the MATLAB platform for mathematical modeling. In the production phase, I conducted performance-oriented studies and developed Deep Learning models using C++ TensorRT, while also integrating algorithms in C/C++ languages for embedded systems. I have made notable contributions to several projects, including: - Anomaly Detection; This project involved detecting faults in high frequency time series sensors based on their compatibility with other sensor data. LSTM based systems from artificial intelligence methods were utilized. - Data Driven Physical Modelling; In this project, complex physical systems were modeled using data science methods, resulting in more robust outcomes than those produced by classical physical models. - Landing Detection; The objective of this project was to rapidly determine the moment of an aircraft’s landing. To achieve this, rule-based solutions were developed using signal processing algorithms for feature extraction. - Missile Evasion Algorithm; This project focused on calculating the optimal path instantly by utilizing optimization methods on models based on the kinematics of the aircraft and the missile.

I was accountable for extracting insights from HR data across more than 70 countries. To enhance the efficiency of this process, I leveraged my expertise in Excel VBA to optimize the creation of KPI reports that were previously prepared manually. This improvement resulted in a reduction of 14 man-hours to just 3 hours, while also preventing potential human errors. Furthermore, I utilized my programming skills in R language to apply and report on various statistical estimation methods that had not been implemented before. I prepared quarterly reports based on these findings and uploaded them to the official website of Allianz, providing critical insights to key stakeholders.

I was employed at Hawa Dawa, a start-up based in Munich that specializes in the development of small-scale devices for measuring air pollution across urban areas. My role at the company was to enhance the accuracy of these devices using machine learning methods. I worked independently on this project, carrying out all of the necessary steps myself. These steps included: - Reading and processing high-frequency sensor data - Implementing feature extraction and selection operations - Creating and comparing machine learning models - Coordinating with the embedded software team during the production phase - Through my efforts, I was able to reduce the error rate from 11.52 units to 2.73 units, resulting in a significant improvement in device accuracy.

I was responsible for the determination of localization methods aimed at minimizing costs for electric vehicles based on population and usage density, in collaboration with both the academic and industrial sectors. This involved negotiating with the business team to ensure that project requirements were met. I developed the entire pipeline using the Python programming language and ran it on a server. The pipeline consisted of the following stages: - Pulling and processing data from APIs to the server. - Preparing graphs and reports to assess the usability and consistency of the obtained data. - Developing the optimization model. - Running the optimization method, transferring the model to Gurobi, and executing it on the server. - Obtaining the system results and ensuring compliance with the project’s initial requirements.