Michael Livingston

Designed a low-latency real-time event processing platform with Apache Flink handling millions of events per day. Built Kafka ingestion with schema enforcement, improved S3 data lake query performance by ~30%, and implemented ACID data management with Apache Iceberg plus stream validation and anomaly detection.

Built large-scale batch and streaming pipelines with Apache Spark (Structured Streaming) processing terabytes of data daily. Orchestrated Airflow DAGs, delivered Snowflake data warehouse solutions reducing query latency by ~25%, optimized Spark execution by ~20% using partitioning and broadcast joins, and enforced data quality with Great Expectations.

Developed Python and NumPy data preprocessing pipelines to process datasets containing hundreds of thousands of records, improving feature extraction and transformation consistency. Built batch data pipelines for relational database loading and improved model evaluation workflows, increasing prediction accuracy by ~10% with unit-tested data transformation components.

Built CI/CD pipelines with Jenkins, reducing deployment time by ~40%. Containerized services with Docker, managed deployments with Kubernetes, provisioned environments with Terraform, and improved monitoring/issue detection time by ~30% using Prometheus and Grafana.

Developed backend services with Java, Spring Boot, and Hibernate to support REST APIs for internal business systems. Designed event-driven communication using Kafka and RabbitMQ for microservices reliability and improved modularity using Spring architecture, backed by JUnit and integration testing.

Assisted in developing web pages using HTML, CSS, JavaScript, and basic Python scripting while learning web development fundamentals. Supported website maintenance tasks, using Git version control and basic debugging while collaborating with development teams.

Earned a bachelor's degree in computer science at Tarleton State University from 2014 to 2018.