Position: Data engineer
Category: Data Engineer
English level: fluent
Years of experience: 1.5


As part of the team responsible for the design and implementation of the Quality Engineering datalake, I worked on the extract, transform, and load (ETL) pipeline to ingest data from various Quality Engineering data sources using Apache NiFi for transformation and storing the data in an AWS S3 bucket. I also configured Glue crawlers and used AWS Athena to create views for data visualization on the AWS QuickSight dashboard. These efforts supported the Quality Engineering team in making data-driven decisions related to quality, enabling informed and strategic decision-making.

 As a member of a machine learning project focused on fraud detection, I was responsible for maintaining the quality and integrity of the data used for training and inferencing. To accomplish this, I utilized Apache NiFi to streamline the cleaning and streaming process of mobile money transaction data to AWS Kinesis data streams, and then loaded the data into an S3 bucket for storage. In addition, I monitored the quality and distribution of the data fed to the ML model to ensure its proper training and accurate inferences. Through my efforts, I contributed to the development of a solution that helps detect and prevent online fraud.

 As a key member of the development team, I played a critical role in the creation and documentation of a hybrid automation suite using Robot Framework. This solution enabled the team to use a single tool for the execution of web, mobile, and API tests, improving efficiency and effectiveness through the establishment of clear structure, coding standards, and clean git work. I am proud to have contributed to this significant achievement for the team.