Add AWS, rm Spotify

.gitignore

@@ -4,7 +4,6 @@ resources/_gen/
 themes/base16*
 
 *.pdf
-*pt*
 
 commit-msg.txt
 .hugo_build.lock

content/resume/_index.md

@@ -3,128 +3,111 @@ title: "Resume"
 date: 2019-02-11T07:50:51-05:00
 draft: false
 ---
+{{% resume/section "Work Experience" %}}<!--- {{{ -->
+
+{{% resume/work-experience name="Red Hat" 
+title="Cloud/Software Engineer Intern" languages="Kubernetes, GoLang, Jenkins" date="May 2020 — Aug 2021" %}}
+
+- **Reduced deployment time by 66%** by implementing a 
+  [solution](https://github.com/apache/incubator-kie-kogito-operator/commit/175a6356c5474f2360ccb8ae835e0b9b2d653cf1) for deploying locally-compiled binaries onto 
+  Kubernetes/OpenShift via command-line, **cutting average 
+  deployment times from 45 minutes to 15 minutes**. 
+  (**Kubernetes/GoLang** used for this and three below). 
+- **Eliminated 80% of manual configuration errors** by enabling 
+  the Kubernetes operator to automatically fetch data from 
+  deployed services and update configurations, **deprecating 
+  legacy startup scripts and reducing overall startup time 
+  by 40%**.
+- **Improved application stability** by introducing startup 
+  probes for legacy applications with longer boot times, 
+  **resulting in a 50% reduction in startup-related failures 
+  and downtime during production launches**.
+- **Enhanced system reliability** by refactoring probes to 
+  [assign default values](https://github.com/apache/incubator-kie-kogito-operator/commit/af4977af228ec8648be28779259d4552246b656f) dynamically based on deployed YAML 
+  files and fixing reconciliation issues, **increasing probe accuracy by 30%** and preventing misconfigurations.
+- **Increased CI pipeline efficiency** by rewriting the 
+  **Jenkins (Groovy)** [nightly pipeline](https://github.com/apache/incubator-kie-kogito-pipelines/commit/4c83f1aecdea2c1ba2796b79839a90d4083dce88) to run in a GitHub PR 
+  environment, allowing for automated testing of all 
+  team-submitted PRs prior to merging, **reducing manual 
+  intervention by 60%**.
+- **Increased project reproducibility** by taking initiative to 
+  write a [reusable GitHub parameters file](https://github.com/apache/incubator-kie-kogito-pipelines/commit/4c83f1aecdea2c1ba2796b79839a90d4083dce88#diff-7d2c018dafbccec859077d19bf1ade53ec9c7649f235528ce89f5632b109f7e6) for the pipeline, 
+  **enabling 100% reusability** and ensuring consistent pipeline 
+  setups across different environments.
+
+{{% /resume/section %}}<!--- }}} -->
+
 {{% resume/section projects %}}<!--- {{{ -->
 
-<!--- RBC AML {{{ -->
+<!--- AWS {{{ -->
 
-{{% resume/project name="AML Risk Analytics" 
+{{% resume/project name="AWS Server"
-languages="Python, SQL, Tableau"  
+url="https://kevin-mok.com/server/" languages="AWS, Kubernetes, Docker, Terraform" date="May 2024" show="true" %}}
-date="July 2025" show="true" %}}
 
-* Built an end-to-end AML simulation using **Python**, 
+- **Deployed and maintained multiple web applications** 
-  generating **9M+ records** across customers, 
+  using **Docker Compose** on **AWS EC2 Debian/Linux servers**, 
-  transactions, and alerts to mimic real-world 
+  ensuring consistent environments for applications handling 
-  financial behavior and suspicious activity patterns.
+  **over 2,000+ monthly requests**.
-* Wrote advanced **SQL (CTEs + joins)** to classify 
+- **Automated AWS infrastructure provisioning** by writing 
-  **high-risk customers**, calculate alert counts, and 
+  **Terraform** files to deploy AWS EC2 instances and Docker 
-  filter transactions over the past 90 days with 
+  containers, **accelerating deployment times by 80%** and 
-  aggregated metrics.
+  providing an easily reproducible infrastructure setup.
-* Engineered a **risk scoring model** in Python 
+- **Improved web application accessibility** by 
-  using transaction thresholds and alert volume to 
+  configuring **AWS Route 53**’s DNS and **NGINX** to route 
-  classify customers as Elevated or Critical risk.
+  subdomains to individual web apps, **enabling seamless 
-* Designed **interactive Tableau dashboards** (Risk 
+  navigation between apps**.
-  Heatmap, Alert Efficiency, Risk vs. Avg Amount) to 
+- **Built a uptime monitoring system** by writing a 
-  visualize cross-country AML exposure and alert 
+  [JavaScript script](https://git.kevin-mok.com/Kevin-Mok/server-pages/src/branch/master/server-pages.service) and setting up a systemd 
-  effectiveness.
+  service/timer to check and display page uptime, 
-- **Developed KPI-ready metrics** (alert rate, avg USD 
+  **ensuring near real-time monitoring and reducing downtime 
-  exposure, transaction volume) to drive AML 
+  time by 95%**.
-  performance reporting and enable cross-country risk 
+- **Enhanced data resilience** by automating regular backups 
-  comparisons.
+  using Amazon EBS snapshots, ensuring **99.9% uptime and data 
-- **Normalized multi-currency transaction data** to 
+  integrity** by creating consistent and reliable backups, 
-  ensure consistent exposure calculations across USD, 
+  **reducing potential data loss by 95%** in disaster scenarios.
-  CAD, and EUR, supporting reliable AML metric 
-  aggregation.
 
 {{% /resume/project %}}
 
-<!--- RBC AML }}} -->
+<!--- AWS }}} -->
-
-<!--- Spotify Visualized {{{ -->
-
-{{% resume/project name="Spotify Visualized" 
-url="https://github.com/Kevin-Mok/astronofty" languages="Python, Django" date="June 2023" 
-show="true" %}}
-
-- **Built a high-performance Python backend** using 
-  Django and PostgreSQL to process 10K+ data records 
-  per user, optimizing ingestion pipelines via API 
-  integration and ORM modeling.
-- **Engineered normalized database schemas** to 
-  streamline query workflows, achieving a **50% 
-  reduction in PostgreSQL latency** for high-volume 
-  reporting tasks.
-- **Visualized user music libraries in Tableau**, 
-  creating dashboards that grouped tracks by **artist 
-  and genre**, enabling users to explore listening 
-  patterns and discover trends in their Spotify data.
-
-{{% /resume/project %}}
-
-<!--- Spotify Visualized }}} -->
 
 <!--- Rarity Surf {{{ -->
 
 {{% resume/project name="Rarity Surf" 
 languages="Python, Django, JavaScript, React"  
-date="Oct 2022" show="true" %}}
+date="Oct 2021" show="true" %}}
 
-- **Built a full-stack reporting tool** using React, 
+- **Developed a full-stack web application** to generate rarity 
-  Django, and **PostgreSQL** to analyze 
+  rankings for NFT's integrated with leading NFT 
-  structured/unstructured metadata from APIs, enabling 
+  marketplace's (OpenSea) API, 
-  real-time rarity scoring and improving insight 
+  enabling users to **quickly identify rare NFT's** and check 
-  delivery by **80%**.
+  their listing status, **improving market research efficiency by 80%**.
-- **Optimized SQL query performance** within a 
+- **Architected a robust Django (Python) [backend](https://github.com/Kevin-Mok/rarity-surf)** to fetch and process 
-  Django-based pipeline, processing NFT ranking data at 
+  NFT metadata from IPFS, store rarity rankings in 
-  scale and exposing results via GraphQL with 
+  **PostgreSQL**, and serve the data via GraphQL API, **ensuring low-latency access and scaling to handle 2,000+ concurrent requests**.
-  **low-latency response times under high concurrency 
-  (2,000+ queries)**.
 
 {{% /resume/project %}}
 
 <!--- Rarity Surf }}} -->
 
-{{% /resume/section %}}<!--- }}} -->
-
-{{% resume/section "Work Experience" %}}<!--- {{{ -->
-
-{{% resume/work-experience name="Red Hat" 
-title="Cloud/Software Engineer Intern" languages="Kubernetes, GoLang, Jenkins" date="May 2022 - Aug 2023" %}}
-
-- **Decreased manual configuration errors by 80%** by 
-  automating service discovery and dynamic config 
-  updates, aligning with AML goals of minimizing 
-  operational risk and improving data integrity.
-- **Enhanced CI pipeline reproducibility and 
-  performance** by rewriting the Jenkins nightly 
-  pipeline to support automated PR-level testing with 
-  reusable parameters, improving report consistency 
-  across environments.
-- **Collaborated cross-functionally** with developers 
-  and testers to maintain reliable infrastructure, 
-  echoing the AML role's emphasis on stakeholder 
-  partnership for building robust reporting systems.
-- **Improved system reliability** during production 
-  launches by implementing startup probes for legacy 
-  services, reducing downtime and enhancing stability 
-  for automated monitoring/reporting pipelines.
-- **Reduced reporting deployment time by 66%** by 
-  building a CLI-based solution to push compiled 
-  binaries directly into Kubernetes/Openshift clusters, 
-  accelerating turnaround for testing and data 
-  validation workflows.
 
 {{% /resume/section %}}<!--- }}} -->
 
 {{% resume/section skills %}}<!--- {{{ -->
 
-**Python**, **SQL**, **PostgreSQL**, **Tableau**, **MongoDB**, **JavaScript**, Django, **React**, Bash, **Git**, **Linux**, **Command Line**, Go(Lang), AWS, Kubernetes, Terraform, Docker (Compose), Jenkins, Groovy, Solidity, C  
+**Python**, **Django**, **JavaScript**, **React**, Node.js, PostgreSQL, MongoDB, Bash, **Git**, **Linux**, **Command Line**, Go(Lang), AWS, Kubernetes, Terraform, Docker (Compose), Jenkins, Groovy, Solidity, C  
 
 {{% /resume/section %}}<!--- }}} -->
 
 {{% resume/section education %}}<!--- {{{ -->
 
 {{% resume/education name="University of Toronto (St. George)"
-title="Computer Science Specialist - 3.84 GPA (CS). Graduated with High Distinction." date="2019 - 2024" %}}
+title="Computer Science Specialist — 3.84 GPA (CS). Graduated with High Distinction." date="2019 — 2024" %}}
+
+{{% /resume/section %}}<!--- }}} -->
+
+{{% resume/section "References" %}}<!--- {{{ -->
+
+{{% resume/references %}}
 
 {{% /resume/section %}}<!--- }}} -->