KYLE CLEVELAND

• Collaborated with cross-functional teams to implement ETL processes.
• Developed and optimized SQL queries for data extraction and analysis.
• Utilized Hive for data retrieval from Hadoop clusters and RedShift.
• Employed Spark SQL for in-depth data exploration.
• Applied feature engineering techniques, including feature intersection and normalization.
• Worked with cloud services, including AWS EMR clusters.
• Deployed recommender systems on AWS infrastructure.
• Utilized Jenkins for automation, enabling CI/CD pipelines as code.
• Applied natural language processing (NLP) techniques to enhance customer satisfaction.
• Designed recommender systems using Collaborative Filtering for personalized course recommendations.

• Implemented CI/CD pipelines with automated software build, test, and deployment, utilizing machine learning for predictive analytics.
• Provisioned cloud infrastructure on AWS, Azure, or GCP, applying data science for scalability and optimization.
• Utilized Docker and Kubernetes for containerization and orchestration, integrating machine learning algorithms for intelligent scaling.
• Developed Python and Shell scripts for task automation, enhancing efficiency through machine learning-driven optimization.
• Ensured system security with machine learning-based threat detection and vulnerability assessments, fostering cross-functional collaboration for innovation

• Integrated DataBricks for advanced data processing and analytics, enhancing decision-making through data science methodologies.

• Collaborated with Data Engineer team for data acquisition, leveraging cloud technologies like Sqoop, Pig, Flume, Hive, MapReduce, and HDFS.

• Demonstrated expertise in Hive by creating user-defined functions (UDFs) for data manipulation.

• Conducted data cleaning, scaling, and engineering with Pandas and Numpy, integrating data science techniques.

• Applied clustering algorithms and advanced time series analysis methods for pattern recognition and forecasting.

• Effectively communicated research findings and recommendations to executive teams, promoting data-driven decision-making.

• Managed data blending, preparation, and publishing using Alteryx and SQL for Tableau consumption, with a focus on cloud integration.

• Ensured system performance, capacity planning, and disaster recovery readiness.

• Maintained up-to-date knowledge of the latest GCP services and best practices, highlighting cloud expertise