big data engineer - 22959

PhD degree in technical science.

• Installed, configured, and debugged test equipment and test cases at customer sites, ensuring optimal network functionality and performance.
• Performed testing of network automation frameworks, providing comprehensive support to customers throughout the testing process.
• Applied testing methodologies to test various networking protocols at the L2/L3 layers, ensuring accurate and efficient network operations.
• Analyzed complex network systems and protocols to identify root causes of defects, enabling targeted issue resolution and improved network performance.
• Created and maintained comprehensive documentation for an open-source test framework dedicated to testing network equipment, hosted on GitHub.

Tools and technologies: Python, Bash, pytest, Docker, LinuxOS, GitHub, Ixia, WireShark, Sphinx, ReadTheDocs.

• Defined requirements and identified appropriate performance testing strategies and approaches for web applications.
• Collaborated with developers to diagnose and fine-tune the performance of high-loaded web applications.
• Utilized collaborative project management tools, load generators, and other monitoring tools for performance monitoring.
• Implemented and maintained back-end and UI performance tests and mock services to simulate highly loaded services.
• Supported and mentored teammates in the role of a tech lead, providing guidance and expertise.
• Designed and maintained project documentation to ensure accurate and up-to-date information.

Tools and technologies: Python, Locust, Behave, Docker, AWS, NewRelic, Kibana, Git, Grafana, InfluxDB, MongoBD, GoCD, BitBucket.

• Collected and cleansed large datasets, applying data wrangling techniques to ensure data quality and consistency.
• Implemented distributed data processing using Spark, allowing for faster and more efficient analysis of big data.
• Developed ETL pipelines using Airflow, ensuring the smooth flow of data from various sources to the target database.
• Collaborated with project managers and analysts to understand their data requirements and deliver optimized data pipelines that supported their KPI tracking needs.
• Actively participated in project documentation design and maintenance, ensuring that all processes and procedures were well-documented and easily accessible to team members.

Tools and technologies: Python, ETL, PySpark, Postgres, Redshift, Alembic, Airflow, AWS Services, Jenkins, BitBucket.