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resume-web
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rbc-aml
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.gitignore
vendored
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.gitignore
vendored
@@ -4,6 +4,7 @@ resources/_gen/
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themes/base16*
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themes/base16*
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*.pdf
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*.pdf
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*pt*
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commit-msg.txt
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commit-msg.txt
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.hugo_build.lock
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.hugo_build.lock
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@@ -578,7 +578,7 @@ header {// {{{
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// }
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// }
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}// }}}
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}// }}}
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h2 {// {{{
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h2 {// {{{
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//color: $base-orange;
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color: $base-orange;
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margin-top: .5rem;
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margin-top: .5rem;
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font-size: 1em;
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font-size: 1em;
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@@ -3,107 +3,128 @@ title: "Resume"
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date: 2019-02-11T07:50:51-05:00
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date: 2019-02-11T07:50:51-05:00
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draft: false
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draft: false
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---
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---
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{{% resume/section "Web Dev Projects" %}}<!--- {{{ -->
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{{% resume/section projects %}}<!--- {{{ -->
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<!--- RBC AML {{{ -->
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{{% resume/project name="AML Risk Analytics"
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languages="Python, SQL, Tableau"
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date="July 2025" show="true" %}}
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* Built an end-to-end AML simulation using **Python**,
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generating **9M+ records** across customers,
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transactions, and alerts to mimic real-world
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financial behavior and suspicious activity patterns.
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* Wrote advanced **SQL (CTEs + joins)** to classify
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**high-risk customers**, calculate alert counts, and
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filter transactions over the past 90 days with
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aggregated metrics.
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* Engineered a **risk scoring model** in Python
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using transaction thresholds and alert volume to
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classify customers as Elevated or Critical risk.
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* Designed **interactive Tableau dashboards** (Risk
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Heatmap, Alert Efficiency, Risk vs. Avg Amount) to
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visualize cross-country AML exposure and alert
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effectiveness.
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- **Developed KPI-ready metrics** (alert rate, avg USD
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exposure, transaction volume) to drive AML
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performance reporting and enable cross-country risk
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comparisons.
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- **Normalized multi-currency transaction data** to
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ensure consistent exposure calculations across USD,
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CAD, and EUR, supporting reliable AML metric
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aggregation.
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{{% /resume/project %}}
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<!--- RBC AML }}} -->
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<!--- Spotify Visualized {{{ -->
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{{% resume/project name="Spotify Visualized"
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url="https://github.com/Kevin-Mok/astronofty" languages="Python, Django" date="June 2023"
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show="true" %}}
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- **Built a high-performance Python backend** using
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Django and PostgreSQL to process 10K+ data records
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per user, optimizing ingestion pipelines via API
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integration and ORM modeling.
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|
- **Engineered normalized database schemas** to
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streamline query workflows, achieving a **50%
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reduction in PostgreSQL latency** for high-volume
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reporting tasks.
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- **Visualized user music libraries in Tableau**,
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creating dashboards that grouped tracks by **artist
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and genre**, enabling users to explore listening
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patterns and discover trends in their Spotify data.
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{{% /resume/project %}}
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<!--- Spotify Visualized }}} -->
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<!--- Rarity Surf {{{ -->
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<!--- Rarity Surf {{{ -->
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{{% resume/project name="Rarity Surf"
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{{% resume/project name="Rarity Surf"
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languages="TypeScript, JavaScript, Node.js, React"
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languages="Python, Django, JavaScript, React"
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date="March 2025" show="true" %}}
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date="Oct 2022" show="true" %}}
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- **Developed a full-stack web application
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- **Built a full-stack reporting tool** using React,
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(TypeScript/JavaScript)** to generate
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Django, and **PostgreSQL** to analyze
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rarity rankings for NFT's, integrating with **leading
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structured/unstructured metadata from APIs, enabling
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marketplace’s API** to enable users to quickly identify
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real-time rarity scoring and improving insight
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rare NFT's and check their listing status, **improving
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delivery by **80%**.
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market research efficiency by 80%**.
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- **Optimized SQL query performance** within a
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- **Built a scalable Node.js backend** with REST API
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Django-based pipeline, processing NFT ranking data at
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endpoints to return NFTs based on customizable filters
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scale and exposing results via GraphQL with
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such as max rank, price, and rarest traits. **Optimized
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**low-latency response times under high concurrency
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performance** to handle **3,000+ concurrent requests** by
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(2,000+ queries)**.
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implementing efficient data fetching and caching
|
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mechanisms using **PostgreSQL** , ensuring low-latency
|
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access to NFT data.
|
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- **Built a dynamic React frontend (TypeScript/JavaScript)** to load and display NFTs in real-time with user-defined filters. Styled
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using a mobile-responsive library, **reducing load times by 50%**.
|
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- **Developed a Discord bot (TypeScript/JavaScript)** to notify users of profitable
|
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||||||
resale opportunities by leveraging historical sales data
|
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||||||
to assess deal quality. This feature **increased user
|
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engagement by 80%** and provided a seamless way for users
|
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to stay updated on market opportunities.
|
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{{% /resume/project %}}
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{{% /resume/project %}}
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<!--- Rarity Surf }}} -->
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<!--- Rarity Surf }}} -->
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<!--- Astronofty {{{ -->
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{{% /resume/section %}}<!--- }}} -->
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{{% resume/project name="Astronofty"
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url="https://github.com/Kevin-Mok/astronofty"
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languages="JavaScript, React, Solidity" date="Jan 2023"
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show="true" %}}
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- **Secured [2nd place](https://devpost.com/software/astronofty) overall out of 150+ teams** at UofTHacks
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X, a 36-hour hackathon, for developing a blockchain-based
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NFT marketplace app.
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- **Built and optimized React (JavaScript) [components](https://github.com/Kevin-Mok/astronofty/tree/main/src/components)** to synchronously
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upload images and metadata to IPFS, **enhancing user engagement by 80%** during the demo.
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{{% /resume/project %}}
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<!--- Astronofty }}} -->
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{{% resume/section "Work Experience" %}}<!--- {{{ -->
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{{% resume/section "Work Experience" %}}<!--- {{{ -->
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{{% resume/work-experience name="Red Hat"
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{{% resume/work-experience name="Red Hat"
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title="Cloud/Software Engineer Intern" languages="Kubernetes, GoLang, Jenkins" date="May 2022 — Aug 2023" %}}
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title="Cloud/Software Engineer Intern" languages="Kubernetes, GoLang, Jenkins" date="May 2022 - Aug 2023" %}}
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- **Reduced deployment time by 66%** by implementing a
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- **Decreased manual configuration errors by 80%** by
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[solution](https://github.com/apache/incubator-kie-kogito-operator/commit/175a6356c5474f2360ccb8ae835e0b9b2d653cf1) for deploying locally-compiled binaries onto
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automating service discovery and dynamic config
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Kubernetes/OpenShift via command-line, **cutting average
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updates, aligning with AML goals of minimizing
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deployment times from 45 minutes to 15 minutes**.
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operational risk and improving data integrity.
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(**Kubernetes/GoLang** used for this and three below).
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- **Enhanced CI pipeline reproducibility and
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- **Eliminated 80% of manual configuration errors** by enabling
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performance** by rewriting the Jenkins nightly
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the Kubernetes operator to automatically fetch data from
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pipeline to support automated PR-level testing with
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deployed services and update configurations, **deprecating
|
reusable parameters, improving report consistency
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legacy startup scripts and reducing overall startup time
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across environments.
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by 40%**.
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- **Collaborated cross-functionally** with developers
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- **Improved application stability** by introducing startup
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and testers to maintain reliable infrastructure,
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probes for legacy applications with longer boot times,
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echoing the AML role's emphasis on stakeholder
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**resulting in a 50% reduction in startup-related failures
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partnership for building robust reporting systems.
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and downtime during production launches**.
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- **Improved system reliability** during production
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- **Enhanced system reliability** by refactoring probes to
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launches by implementing startup probes for legacy
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[assign default values](https://github.com/apache/incubator-kie-kogito-operator/commit/af4977af228ec8648be28779259d4552246b656f) dynamically based on deployed YAML
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services, reducing downtime and enhancing stability
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files and fixing reconciliation issues, **increasing probe accuracy by 30%** and preventing misconfigurations.
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for automated monitoring/reporting pipelines.
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- **Increased CI pipeline efficiency** by rewriting the
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- **Reduced reporting deployment time by 66%** by
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**Jenkins (Groovy)** [nightly pipeline](https://github.com/apache/incubator-kie-kogito-pipelines/commit/4c83f1aecdea2c1ba2796b79839a90d4083dce88) to run in a GitHub PR
|
building a CLI-based solution to push compiled
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environment, allowing for automated testing of all
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binaries directly into Kubernetes/Openshift clusters,
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team-submitted PRs prior to merging, **reducing manual
|
accelerating turnaround for testing and data
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intervention by 60%**.
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validation workflows.
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- **Increased project reproducibility** by taking initiative to
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write a [reusable GitHub parameters file](https://github.com/apache/incubator-kie-kogito-pipelines/commit/4c83f1aecdea2c1ba2796b79839a90d4083dce88#diff-7d2c018dafbccec859077d19bf1ade53ec9c7649f235528ce89f5632b109f7e6) for the pipeline,
|
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||||||
**enabling 100% reusability** and ensuring consistent pipeline
|
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||||||
setups across different environments.
|
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- **Streamlined developer onboarding** by authoring
|
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||||||
comprehensive [project documentation](https://github.com/apache/incubator-kie-kogito-operator/blob/1534c03d1d26bec08a16608a775782bf8b305de9/docs/GUIDE_FOR_KOGITO_DEVS.md) and mentoring an
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incoming intern, **reducing onboarding time by 50%** and
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enhancing new team members' productivity within their
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first sprint.
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{{% /resume/section %}}<!--- }}} -->
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{{% /resume/section %}}<!--- }}} -->
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{{% /resume/section %}}<!--- }}} -->
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{{% resume/section skills %}}<!--- {{{ -->
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{{% resume/section skills %}}<!--- {{{ -->
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**TypeScript**, **JavaScript**, **React**, **Node.js**, **Python**, **Django**, PostgreSQL, MongoDB, Bash, **Git**, **Linux**, **Command Line**, Go(Lang), AWS, Kubernetes, Terraform, Docker (Compose), Jenkins, Groovy, Solidity, C
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**Python**, **SQL**, **PostgreSQL**, **Tableau**, **MongoDB**, **JavaScript**, Django, **React**, Bash, **Git**, **Linux**, **Command Line**, Go(Lang), AWS, Kubernetes, Terraform, Docker (Compose), Jenkins, Groovy, Solidity, C
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|
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{{% /resume/section %}}<!--- }}} -->
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{{% /resume/section %}}<!--- }}} -->
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{{% resume/section education %}}<!--- {{{ -->
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{{% resume/section education %}}<!--- {{{ -->
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{{% resume/education name="University of Toronto (St. George)"
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{{% resume/education name="University of Toronto (St. George)"
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title="Computer Science Specialist — 3.84 GPA (CS). Graduated with High Distinction." date="2019 — 2024" %}}
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title="Computer Science Specialist - 3.84 GPA (CS). Graduated with High Distinction." date="2019 - 2024" %}}
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{{% /resume/section %}}<!--- }}} -->
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{{% /resume/section %}}<!--- }}} -->
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@@ -1,39 +0,0 @@
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# ME Sniper
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write me a resume section similar to this (just a bit longer) for a web dev resume based on the points after with made up statistics
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## Old
|
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- **Developed a full-stack web application** to generate rarity
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rankings for NFT's integrated with leading NFT
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marketplace's (OpenSea) API,
|
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enabling users to **quickly identify rare NFT's** and check
|
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their listing status, **improving market research efficiency by 80%**.
|
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- **Architected a robust Django (Python) [backend](https://github.com/Kevin-Mok/rarity-surf)** to fetch and process
|
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NFT metadata from IPFS, store rarity rankings in
|
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**PostgreSQL**, and serve the data via GraphQL API, **ensuring low-latency access and scaling to handle 2,000+ concurrent requests**.
|
|
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- **Developed a dynamic React (Javascript)
|
|
||||||
[frontend](https://github.com/Kevin-Mok/rarity-surf-frontend)** using hooks to load
|
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||||||
rarity data in real-time, styled with Tailwind for
|
|
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mobile responsiveness, **improving user experience
|
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||||||
and reducing frontend load times by 70%**.
|
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|
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## New
|
|
||||||
- Developed a full-stack web application to generate rarity rankings for NFT’s integrated with leading NFT marketplace’s (Magic
|
|
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Eden) API, enabling users to quickly identify rare NFT’s and check their listing status, improving market research efficiency by 80%.
|
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- fetch metadata from either IPFS or website in parallel processes to create rarity
|
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rankings as soon as metadata revealed
|
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- reverse engineered algorithm for rarity rankings for NFT's based on article from
|
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marketplace about their in-house statistical rarity
|
|
||||||
ranking
|
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- created Prisma schema for PostgreSQL for database to store NFT data
|
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- Node.js backend with API endpoints to return NFT's based
|
|
||||||
on max rank/price along with rarest traits
|
|
||||||
- lowest prices for rarity percentile to see if good deal
|
|
||||||
- fetch all listings from leading marketplace (Magic Eden) to be
|
|
||||||
able to identify which rare NFT's are on sale and be able
|
|
||||||
to filter based on max price/filter
|
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- store previous sales data to check whether a buy at rarity
|
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percentile is a good deal
|
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- React FE to dynamically load NFT's based on rarity
|
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||||||
rank/price filter with ability to hide seen ones
|
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||||||
- Discord bot to notify you when customizable profitable resale
|
|
||||||
opportunity comes up based on rarity level/price
|
|
||||||
@@ -52,41 +52,6 @@ date="Oct 2021" show="true" %}}
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|
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||||||
<!--- Rarity Surf }}} -->
|
<!--- Rarity Surf }}} -->
|
||||||
|
|
||||||
<!--- Rarity Surf {{{ -->
|
|
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|
|
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{{% resume/project name="Rarity Surf (2)"
|
|
||||||
languages="Typescript, Node.js, React"
|
|
||||||
date="" show="true" %}}
|
|
||||||
|
|
||||||
- **Developed a full-stack web application** to generate
|
|
||||||
rarity rankings for NFT's, integrating with **leading
|
|
||||||
marketplace’s API** to enable users to quickly identify
|
|
||||||
rare NFT's and check their listing status, **improving
|
|
||||||
market research efficiency by 80%**.
|
|
||||||
- **Built a scalable Node.js backend** with REST API
|
|
||||||
endpoints to return NFTs based on customizable filters
|
|
||||||
such as max rank, price, and rarest traits. **Optimized
|
|
||||||
performance** to handle **3,000+ concurrent requests** by
|
|
||||||
implementing efficient data fetching and caching
|
|
||||||
mechanisms, ensuring low-latency access to NFT data.
|
|
||||||
- **Developed a dynamic React frontend** to load and display
|
|
||||||
NFT's in real-time based on user-defined filters to
|
|
||||||
streamline browsing. Styled the interface using **Tailwind
|
|
||||||
CSS** for a responsive and modern design, **reducing
|
|
||||||
frontend load times by 50%**.
|
|
||||||
- **Developed a Discord bot** to notify users of profitable
|
|
||||||
resale opportunities by leveraging historical sales data
|
|
||||||
to assess deal quality. This feature **increased user
|
|
||||||
engagement by 80%** and provided a seamless way for users
|
|
||||||
to stay updated on market opportunities.
|
|
||||||
- Designed and implemented a **PostgreSQL schema** for to
|
|
||||||
efficiently store NFT data, including metadata, rarity
|
|
||||||
scores, and historical sales data.
|
|
||||||
|
|
||||||
{{% /resume/project %}}
|
|
||||||
|
|
||||||
<!--- Rarity Surf }}} -->
|
|
||||||
|
|
||||||
<!--- Astronofty {{{ -->
|
<!--- Astronofty {{{ -->
|
||||||
|
|
||||||
{{% resume/project name="Astronofty"
|
{{% resume/project name="Astronofty"
|
||||||
@@ -177,6 +142,7 @@ url="https://kevin-mok.com/server/" languages="AWS, Kubernetes, Docker, Terrafor
|
|||||||
|
|
||||||
<!--- AWS 3 }}} -->
|
<!--- AWS 3 }}} -->
|
||||||
|
|
||||||
|
|
||||||
<!--- Astronofty (extended) {{{ -->
|
<!--- Astronofty (extended) {{{ -->
|
||||||
|
|
||||||
{{% resume/project name="Astronofty"
|
{{% resume/project name="Astronofty"
|
||||||
Submodule static/pdf updated: 5224501fcf...6d0677da34
Reference in New Issue
Block a user