Senior Data Engineer

As a skilled Data Engineer, main responsibilities are:

Utilizing AWS cloud technologies and Python to design and maintain efficient data pipelines for a prominent company in the diamond industry

Effectively handling and processing large-scale datasets

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

Developing custom scripts and libraries to automate data processing tasks and ensure high data quality checks

Proficient in working with renowned data processing frameworks

Proficiency in AWS services such as S3, Redshift, Glue, and Lambda has enabled the creation of efficient, scalable, and fault-tolerant data processing pipelines

Responsible for creating clean source data for Tableau reports presented to C-level managers, providing them with valuable insights into the business

Engaged in projects aimed at improving profit margins and productivity by executing initiatives that align with the goals and expectations of our business partners

Engaged in effective stakeholder communication throughout the project development phase

Working in the finance domain involves the development and implementation of artificial intelligence-based estimation methods, which enhances the operational capabilities of our business lines

Leveraged cloud-based technologies, such as Amazon Web Server, employing services such as SageMaker, Glue, and Lambda for project development and production stages

Was responsible for overseeing a team of employees to achieve organizational growth objectives

Conducted meetings with senior managers to determine project requirements and guide engineers and technical staff in alignment with these requirements

Organized Scrum meetings and ensured that project progress remained in line with the overall business plan

Contributed to several projects to enhance 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

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

Relied heavily on data science libraries such as scikit-learn, TensorFlow, and Keras, and generally utilized the MAT- LAB platform for mathematical modeling

Conducted performance-oriented studies and developed Deep Learning models using C++ TensorRT, while also integrating algorithms in C/C++ languages for embedded systems

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 determine the moment of an aircraft’s landing rapidly. 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.