|
|
|
@@ -3,111 +3,128 @@ title: "Resume"
|
|
|
|
date: 2019-02-11T07:50:51-05:00
|
|
|
|
date: 2019-02-11T07:50:51-05:00
|
|
|
|
draft: false
|
|
|
|
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%**.
|
|
|
|
|
|
|
|
- **Demonstrated leadership and collaboration** by actively
|
|
|
|
|
|
|
|
contributing to Agile sprint planning in a 12-member team,
|
|
|
|
|
|
|
|
driving improvement in sprint velocity through
|
|
|
|
|
|
|
|
optimized task delegation and idea generation.
|
|
|
|
|
|
|
|
- **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.
|
|
|
|
|
|
|
|
- **Streamlined developer onboarding** by authoring
|
|
|
|
|
|
|
|
comprehensive [project documentation](https://github.com/apache/incubator-kie-kogito-operator/blob/1534c03d1d26bec08a16608a775782bf8b305de9/docs/GUIDE_FOR_KOGITO_DEVS.md) and mentoring an
|
|
|
|
|
|
|
|
incoming intern, **reducing onboarding time by 50%** and
|
|
|
|
|
|
|
|
enhancing new team members' productivity within their
|
|
|
|
|
|
|
|
first sprint.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
{{% /resume/section %}}<!--- }}} -->
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
{{% resume/section projects %}}<!--- {{{ -->
|
|
|
|
{{% resume/section projects %}}<!--- {{{ -->
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<!--- RBC AML {{{ -->
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
{{% resume/project name="AML Risk Analytics"
|
|
|
|
|
|
|
|
languages="Python, SQL, Tableau"
|
|
|
|
|
|
|
|
date="July 2025" show="true" %}}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
* Built an end-to-end AML simulation using **Python**,
|
|
|
|
|
|
|
|
generating **9M+ records** across customers,
|
|
|
|
|
|
|
|
transactions, and alerts to mimic real-world
|
|
|
|
|
|
|
|
financial behavior and suspicious activity patterns.
|
|
|
|
|
|
|
|
* Wrote advanced **SQL (CTEs + joins)** to classify
|
|
|
|
|
|
|
|
**high-risk customers**, calculate alert counts, and
|
|
|
|
|
|
|
|
filter transactions over the past 90 days with
|
|
|
|
|
|
|
|
aggregated metrics.
|
|
|
|
|
|
|
|
* Engineered a **risk scoring model** in Python
|
|
|
|
|
|
|
|
using transaction thresholds and alert volume to
|
|
|
|
|
|
|
|
classify customers as Elevated or Critical risk.
|
|
|
|
|
|
|
|
* Designed **interactive Tableau dashboards** (Risk
|
|
|
|
|
|
|
|
Heatmap, Alert Efficiency, Risk vs. Avg Amount) to
|
|
|
|
|
|
|
|
visualize cross-country AML exposure and alert
|
|
|
|
|
|
|
|
effectiveness.
|
|
|
|
|
|
|
|
- **Developed KPI-ready metrics** (alert rate, avg USD
|
|
|
|
|
|
|
|
exposure, transaction volume) to drive AML
|
|
|
|
|
|
|
|
performance reporting and enable cross-country risk
|
|
|
|
|
|
|
|
comparisons.
|
|
|
|
|
|
|
|
- **Normalized multi-currency transaction data** to
|
|
|
|
|
|
|
|
ensure consistent exposure calculations across USD,
|
|
|
|
|
|
|
|
CAD, and EUR, supporting reliable AML metric
|
|
|
|
|
|
|
|
aggregation.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
{{% /resume/project %}}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<!--- RBC AML }}} -->
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<!--- 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 {{{ -->
|
|
|
|
<!--- Rarity Surf {{{ -->
|
|
|
|
|
|
|
|
|
|
|
|
{{% resume/project name="Rarity Surf"
|
|
|
|
{{% resume/project name="Rarity Surf"
|
|
|
|
languages="Python, JavaScript, React, Django"
|
|
|
|
languages="Python, Django, JavaScript, React"
|
|
|
|
date="Oct 2021" show="true" %}}
|
|
|
|
date="Oct 2022" show="true" %}}
|
|
|
|
|
|
|
|
|
|
|
|
- **Developed a full-stack web application** to generate rarity
|
|
|
|
|
|
|
|
rankings for NFT's integrated with OpenSea's API,
|
|
|
|
|
|
|
|
enabling users to **quickly identify rare NFT's** and check
|
|
|
|
|
|
|
|
their listing status, **improving market research efficiency by 80%**.
|
|
|
|
|
|
|
|
- **Architected a robust Django (Python) [backend](https://github.com/Kevin-Mok/rarity-surf)** to fetch and process
|
|
|
|
|
|
|
|
NFT metadata from IPFS, store rarity rankings in
|
|
|
|
|
|
|
|
**PostgreSQL**, and serve the data via GraphQL API, **ensuring low-latency access and scaling to handle 2,000+ concurrent requests**.
|
|
|
|
|
|
|
|
- **Developed a dynamic React [frontend](https://github.com/Kevin-Mok/rarity-surf-frontend)** using hooks to load
|
|
|
|
|
|
|
|
rarity data in real-time, styled with Tailwind for
|
|
|
|
|
|
|
|
mobile responsiveness, **improving user experience
|
|
|
|
|
|
|
|
and reducing frontend load times by 70%**.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
- **Built a full-stack reporting tool** using React,
|
|
|
|
|
|
|
|
Django, and **PostgreSQL** to analyze
|
|
|
|
|
|
|
|
structured/unstructured metadata from APIs, enabling
|
|
|
|
|
|
|
|
real-time rarity scoring and improving insight
|
|
|
|
|
|
|
|
delivery by **80%**.
|
|
|
|
|
|
|
|
- **Optimized SQL query performance** within a
|
|
|
|
|
|
|
|
Django-based pipeline, processing NFT ranking data at
|
|
|
|
|
|
|
|
scale and exposing results via GraphQL with
|
|
|
|
|
|
|
|
**low-latency response times under high concurrency
|
|
|
|
|
|
|
|
(2,000+ queries)**.
|
|
|
|
|
|
|
|
|
|
|
|
{{% /resume/project %}}
|
|
|
|
{{% /resume/project %}}
|
|
|
|
|
|
|
|
|
|
|
|
<!--- Rarity Surf }}} -->
|
|
|
|
<!--- Rarity Surf }}} -->
|
|
|
|
|
|
|
|
|
|
|
|
<!--- AWS {{{ -->
|
|
|
|
{{% /resume/section %}}<!--- }}} -->
|
|
|
|
|
|
|
|
|
|
|
|
{{% resume/project name="AWS Server"
|
|
|
|
{{% resume/section "Work Experience" %}}<!--- {{{ -->
|
|
|
|
url="https://kevin-mok.com/server/" languages="AWS, Kubernetes, Docker, Terraform" date="May 2024" show="true" %}}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
- **Deployed and maintained multiple web applications**
|
|
|
|
{{% resume/work-experience name="Red Hat"
|
|
|
|
using **Docker Compose** on **AWS EC2 Debian/Linux servers**,
|
|
|
|
title="Cloud/Software Engineer Intern" languages="Kubernetes, GoLang, Jenkins" date="May 2022 - Aug 2023" %}}
|
|
|
|
ensuring consistent environments for applications handling
|
|
|
|
|
|
|
|
**over 2,000+ monthly requests**.
|
|
|
|
|
|
|
|
- **Automated AWS infrastructure provisioning** by writing
|
|
|
|
|
|
|
|
**Terraform** files to deploy AWS EC2 instances and Docker
|
|
|
|
|
|
|
|
containers, **accelerating deployment times by 80%** and
|
|
|
|
|
|
|
|
providing an easily reproducible infrastructure setup.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
{{% /resume/project %}}
|
|
|
|
- **Decreased manual configuration errors by 80%** by
|
|
|
|
|
|
|
|
automating service discovery and dynamic config
|
|
|
|
<!--- AWS }}} -->
|
|
|
|
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 %}}<!--- }}} -->
|
|
|
|
|
|
|
|
|
|
|
|
{{% resume/section skills %}}<!--- {{{ -->
|
|
|
|
{{% resume/section skills %}}<!--- {{{ -->
|
|
|
|
|
|
|
|
|
|
|
|
**JavaScript**, **React**, **Python**, **Django**, Node.js, PostgreSQL, MongoDB, Bash, **Git**, **Linux**, **Command Line**, Go(Lang), AWS, Kubernetes, Terraform, Docker (Compose), Jenkins, Groovy, Solidity, C
|
|
|
|
**Python**, **SQL**, **PostgreSQL**, **Tableau**, **MongoDB**, **JavaScript**, Django, **React**, Bash, **Git**, **Linux**, **Command Line**, Go(Lang), AWS, Kubernetes, Terraform, Docker (Compose), Jenkins, Groovy, Solidity, C
|
|
|
|
|
|
|
|
|
|
|
|
{{% /resume/section %}}<!--- }}} -->
|
|
|
|
{{% /resume/section %}}<!--- }}} -->
|
|
|
|
|
|
|
|
|
|
|
|
{{% resume/section education %}}<!--- {{{ -->
|
|
|
|
{{% resume/section education %}}<!--- {{{ -->
|
|
|
|
|
|
|
|
|
|
|
|
{{% resume/education name="University of Toronto"
|
|
|
|
{{% 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 %}}<!--- }}} -->
|
|
|
|
{{% /resume/section %}}<!--- }}} -->
|
|
|
|
|
|
|
|
|
|
|
|
|