Jordan Moore

Led feature development for the Adobe Cloud Services Platform on Kubernetes, managing an ecosystem of hundreds of production clusters.

Reduced support ticket volume by over 25% through the development and deployment of a custom GPT chatbot, leveraging LLM tokens from internal documentation.

Engineered and deployed tooling to facilitate seamless migration of managed applications across ArgoCD instances, enhancing infrastructure flexibility.

Automated zero-to-production developer onboarding by creating Backstage Templates and Actions, significantly streamlining the development lifecycle.

Engineered robust tooling and extensions for an Internal Developer Platform (IDP), enhancing core cloud platform capabilities.

Provided critical 24/7 support and on-call services to internal Cloud Platform clients, ensuring high availability and operational efficiency.

Maintained and optimized scalable, multi-tenant software templates, driving standardization and efficiency across diverse business units.

Championed and implemented container-based CI/CD and development workflows, delivering hands-on training that improved adoption and developer productivity.

Developed critical Commerce Cloud administration plugins within Adobe Experience Cloud, enhancing platform manageability and user experience.

Spearheaded the development of the Commerce Cloud Terraform Provider, enabling infrastructure as code for scalable e-commerce solutions.

Significantly improved monitoring capabilities for Magento PWA Studio and AEM Cloud Manager, leading to proactive issue detection.

Optimized AEM Cloud Manager's test-suite runtime by over 85%, accelerating release cycles and improving developer efficiency.

Designed and implemented an availability zone-aware multi-cloud system, abstracting Docker schedulers (Marathon, Nomad, Kubernetes) for enhanced resilience and portability.

Successfully orchestrated the migration of Apache Mesos workloads to Nomad, and subsequently to Kubernetes, optimizing resource utilization and scalability.

Engineered and deployed robust Big Data, event-driven architectures, microservices, and Kafka environments for a diverse client portfolio.

Developed compelling pre-sales demonstrations utilizing Vagrant, Terraform, Docker orchestration, Ansible, and Bash, effectively showcasing platform capabilities.

Leveraged Elasticsearch, Logstash, and Kibana for advanced analytical and log-parsing, providing critical insights for client operations.

Administered and maintained a complex hybrid-cloud environment, including a 100+ node on-premise Hadoop cluster and multiple Kafka clusters across multi-cloud environments for a vacation-rental client, ensuring high performance.

Provided comprehensive 24/7 on-call support, resolving infrastructure and developer issues, managing maintenance notifications, and conducting debugging sessions to minimize downtime.

Converted plaintext Oracle database exports to Apache Parquet format using Apache Pig scripts, optimizing data storage and access for an international retail client.

Accelerated the client's Oracle Pro*C ETL process runtime by 90% through strategic implementation of Apache Impala and Parquet, significantly boosting data processing efficiency.

Built a high-performance Spark Streaming ETL pipeline for a multi-national payment processing client, enabling real-time data ingestion and transformation.

Engineered custom Hadoop InputFormats with robust regex support to efficiently parse semi-structured documents into Spark RDDs, improving data ingestion flexibility.

Leveraged Apache Hive, MemSQL, and Elasticsearch to perform complex queries on payment transaction details, facilitating comprehensive data analysis.

Transformed tabular data into a graph-optimized format for a global technology client, enhancing data relationship analysis.

Conducted a thorough comparative analysis of Neo4j and OrientDB, evaluating their capabilities for graph database solutions.

Presented and advocated for graph database solutions to identify connected components within web traffic metadata, enabling highly targeted advertising strategies.

Refined an iterative Random Forest algorithm to accurately categorize transcribed call-center issues for a global technology client, improving operational efficiency.

Developed and deployed a machine learning model that achieved over 80% accuracy in categorizing call-center issues, exceeding client performance targets.

Optimized machine learning model performance through meticulous manual tuning of parameters, significantly enhancing predictive accuracy.