Ghibli-style illustration of Jay coding

Jay

Data Engineer & BI Engineer

🇨🇦 Toronto, Canada

👋 Self Introduction

I'm a Data Engineer with ~9 years of experience building large-scale data systems.

I previously worked at NAVER, where I built data platforms supporting 20M+ daily active users across multiple services.

My focus is designing event-driven data systems and lakehouse architectures that transform high-volume event data into reliable, analytics-ready datasets for AI and business use cases.

Beyond infrastructure, I care deeply about how data is used — building derived datasets, defining metrics, and enabling teams with self-serve analytics and dashboards.

Recently, I've been working on LLM-based data systems in personal projects after relocating to Canada, focusing on turning unstructured data into structured, usable formats.

Always open to conversations around data platforms, analytics, or AI systems.

Portfolio

Personal projects

July 2025 – present

AI-Driven Problem Solving Project

🛠️ Key Technical Skills

Data Processing & Analytics

PySpark SQL Python Pandas

Data Pipeline & Orchestration

Apache Airflow Apache Kafka Apache Flink ETL/ELT Data Pipeline

Data Storage & Architecture

RDBMS OLAP DB Column DB Data Lake Data Warehouse Lakehouse

Cloud & Infrastructure

Docker Kubernetes

BI & Visualization

Tableau Metabase

💼 Career

2025.07 - Present

Break Period

Break period due to relocation

🇨🇦 Toronto, Canada
2020.04 - 2025.07

Data Engineer (BI Focused)

Data engineering and BI system development

🏢 Company Name
2016.09 - 2020.04

Data Engineer (BI Focused)

Data engineering and BI system development

🏢 ZUM Internet

🎓 Education

Master's

Hongik University

Computer Engineering (Big Data)

2014 - 2016

Bachelor's

Hongik University

Computer Engineering

2009 - 2013

📝 Blog Purpose

Started this blog to organize and share the knowledge and experience gained from over 8 years of data engineering work in Korea.

During this break period for settling in Toronto, I aim to systematically organize past experiences and explore the evolving world of data engineering along with current trends.

🎯 Topics to Cover

Data Engineering Overview

Data pipelines, architecture, best practices

Current Trends

Latest technologies, tools, architecture patterns

BI Analytics

Data analysis methodologies and insight generation

BI Tools

Utilization and comparison of various BI tools

📧 Contact