Spark Developer Resume Sample

-Creating and refining machine learning models to enhance online fraud prevention capabilities.
-Building and maintaining data lakes and Big Data pipelines to facilitate efficient data processing and analysis.
-Developing robust, scalable software solutions suitable for large-scale deployment in a cloud environment.
-Utilizing technologies such as Docker, AWS, Java 11, Kubernetes, and Apache Spark to manage and optimize cloud-based systems.
-Working closely with remote teams across multiple countries to ensure seamless project integration and delivery.
-Leading R&D initiatives to explore new methodologies and technologies for improving cybersecurity measures.

-Working with Big Data, in particular financial, using Apache Spark and Apache Hadoop.
-Performing advanced analysis with multiple types of data (text, images, etc.).
-Working on a daily basis with Apache Hive and Apache Hue.
-Designing and developing of Python based full-stack software.
-Visualization, both using custom-made software and using tools such as Microsoft PowerBI.
-Decomposing needs expressed by business owners to requirements and software design.
-Enforcing clean code rules along colleagues
Technologies: Python 3, Apache Spark, Apache Hadoop, Tensorflow, PowerBI, Apache Hive, Apache Hue

-Developing streaming applications using Apache Flink.
-Deploying streaming jobs via Mesos / DCOS / Kubernetes.
-Developing real time system for behavioural analysis.
-Integrating Machine Learning algorithms with existing stream processing system.
Technologies: Apache Flink, Apache Kafka, Apache Hadoop, Apache Mesos, DC/OS, H2O.ai, Google Protocol Buffers, Kubernetes, Apache Nifi

-Supervision over a group of programmers and testers (8 people in total).
-Implementing Agile methodologies and providing team coaching to enhance productivity and collaboration.
-Facilitating sprint planning sessions, reviews, and retrospectives to ensure continuous improvement.
-Maintaining constant communication with the project owner to align project goals and deliverables.

-Design, implementation and deployment of applications for image analysis for the recognition of license plates.
-Parameter tuning for poor lighting conditions, noise in data and uncertain knowledge.
-Creation and implementation of algorithms to improve the quality of solutions thanks to grouping algorithms and the data mining.
-Creating a website for streaming text analysis from LPR cameras (License Plate Recognition), integration with existing systems.
-Real time data analysis, developing streaming algorithms.
-Technologies: Java 8, Python 3, Spring Boot, Akka, AWS, Ansible, Jenkins, Maven, Atlassian, OpenCV, TensorFlow, Git, Bitbucket, Couchbase NoSQL, JSON, XML.

-Leading research projects in machine learning, data analysis, and system design, contributing to academic publications and advancements in the field.
-Delivering lectures, designing course materials, and mentoring students in computer science topics, ensuring comprehensive understanding and academic success.
-Creating and updating curriculum for courses in machine learning, Java programming, and data analysis to keep up with industry trends and academic standards.
-Guiding students through their research projects and theses, providing expertise and feedback to ensure high-quality work and successful completion.

-Researching and developing software solutions for the analysis of high-volume aviation data, ensuring accurate and efficient processing.
-Designing and implementing batch and streaming data processing systems to handle large datasets effectively.
-Developing and optimizing error compensation algorithms to improve data accuracy and reliability.
-Creating comprehensive data visualization tools to represent complex datasets in an intuitive and actionable manner.

-Supporting professors in delivering lectures and conducting practical sessions, ensuring students understand the material.
-Evaluating and providing feedback on student assignments, projects, and exams to maintain academic standards.
-Overseeing laboratory exercises, helping students with hands-on activities and experiments related to computer science topics.