Last Updated on 7th April 2026
Jordan Moore
https://www.linkedin.com/in/jordanmoorerhit/
 
EXPERTISE
CLOUD

S3 VPC IAM CloudWatch
Route53 EC2 EKS
CodeBuild ECR • EMR
MICROSERVICES
Docker Kubernetes Mesos +
Marathon Nomad Consul Vault
DATABASES
MongoDB MySQL PostgreSQL
ElasticSearch
CI/CD
ArgoCD, Events, & Workows
GitHub Actions Ansible Puppet
Terraform Jenkins Vagrant
SKILLS
ENVIRONMENTS
Backend APIs Tool Development
Data Analysis Architecture
Platform Engineering
PROGRAMMING
Java, Scala Maven, Gradle, SBT
JAX-RS Dropwizard Spring Boot
Jackson Lombok
Python FastAPI Flask Requests
Beautifulsoup Pydantic
NodeJS TypeScript ReactJS
HTML/CSS JQuery Bootstrap
PHP

Go Android
EDUCATION
ROSE-HULMAN INSTITUTE
OF TECHNOLOGY
  

Double Major in Software
Engineering and Computer
Science
Minor in Mathematics and
Computational Science
  
EXPERIENCE
ADOBE INC.   
     
Feature development for Adobe Cloud Services Platform on Kubernetes, overseeing
an ecosystem of hundreds of clusters
Built and reviewed extensions and software templates for a Backstage-based
Internal Developer Platform (IDP)
Performed support & on-call services for company-wide Cloud Platform users
Contributed to golden-template Helm Charts and Argo Workow CI/CD container
actions
> DEVELOPER PLATFORMS    
Deected 25+% of support tickets with a Python/FastAPI Retrieval Augmented
Generation (RAG) GPT chatbot connected to internal documentation and JIRA
tickets
Led Golang CLI solution to migrate applications between ArgoCD instances with
zero downtime
Developed Backstage Templates and Actions to automate zero-to-production
developer onboarding
> MAGENTO    
Improved Java Spring Boot test-suite runtime for AEM Cloud Manager by 85+%
Led development of Commerce Cloud Golang HTTP Client and Terraform
Provider
Developed Commerce Cloud administration plugins within Adobe Experience
Cloud using Node.js REST API with GraphQL
Enhanced Prometheus metrics and Grafana dashboards for AWS-hosted
Magento PWA Studio & AEM Cloud Manager environments
INSPIRED INTELLECT  
     
Consulted for Fortune 500 clients on Big Data and Kafka event-driven microservice
architectures
Architected pre-sales demos using Ansible, Docker, Terraform, and Vagrant
> CLOUD DEVOPS & RELIABILITY ENGINEER    
Developed a Java REST API with React.js UI to schedule containers across four
different Docker orchestrators
Documented playbooks, prompts, and patterns in an internal knowledge base
helping the whole organization move faster
Integrated CI/CD pipelines with Jenkins and Spinnaker to roll out microservice
releases
> HADOOP & KAFKA ADMINISTRATOR    
Administered a hybrid-cloud Hadoop-AWS environment with over a hundred
nodes and a global Kafka-connected event system
Provided on-call support for infrastructure and developer questions, maintenance
notications, and debugging sessions
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Jordan Moore
https://www.linkedin.com/in/jordanmoorerhit/
 
EXPERIENCE (CONT.)
> HADOOP DATA WAREHOUSE OFFLOADER   
Converted plain-text Oracle database exports into Apache Parquet using Apache Pig
scripts for an international retail client
Improved runtime of the client’s Oracle Pro*C analytics process by 90% using
Apache Impala and Parquet
> APACHE SPARK STREAMING DEVELOPER   
Migrated daily batch processing into a real-time Spark Streaming Scala ETL pipeline
for a multi-national payment processing client
Wrote custom Hadoop InputFormats with RegEx support to parse semi-structured
documents into Spark RDDs for distributed parallel-processing
> GRAPH DATABASE ANALYST   
Wrote a Java importer to join six disparate, tabular datasets within Neo4j and
OrientDB to yield a 10% improvement in targeted advertising
> APACHE SPARK MLLIB DEVELOPER   
Rened an iterative Random Forest algorithm using Spark Scala to categorize textual
call-center conversations for a worldwide technology client
Manually tuned the machine-learning model to yield 80+% related documentation
recommendation accuracy
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