CakeResume Talent Search

with reliable data, and saved 50% effort (Groovy, Dataproc, Spark, BigQuery, EMR, Hive, Jenkins) • Designed ETL pipeline framework and implemented it to cloud service and held user training sessions for engineers and analysts. This project was reduced by 40% maintenance efforts and increased by 60% deployment efficiency by leveraging Airflow and Kubernetes (Gitlab, Airflow, Python, K8s) Developed and deployed API to retrieve data in the cloud environment • Developed an API to retrieve data that included geo-location data from BigQuery and deployed it on the GCP environment. The API saved 80% of the time on fetching data (Cloud

Data Scientist Intern @ Cathay Financial Holdings. 國泰金控 Digital data & Technology (DDT, 數數發) Research into Interpretable Machine Learning and its existing algorithms. Experimented LIME & SHAP on open data. ( GitHub ) Real Estate Evaluation model – Geographical/Credit Card data gathering, cleaning, feature engineering (Hit-rate performance improved from 55% to 70%) Python Machine learning Data Pipelines Web Crawlers PyTorch, Airflow, Pandas, docker SQL Extract-Transform-Load(ETL) Efficiency(Window Functions) Language Chinese(Native) English (TOEIC 905/990 TOEFL 96/120) Project(@ Esun only) ( Powerpoint demo link, clic...

import traditional CSV files. Utilized Databricks for large-scale data processing, leveraging its Spark capabilities to efficiently transform and aggregate incoming data streams. With the combined power of Databricks and AWS Lambda, ensured unparalleled data consistency, quality, and preparedness for sophisticated analytics and reporting. Utilized Databricks and Airflow to run extensive data profiling tasks, analyzing data patterns and identifying potential quality issues before they reached the Databricks Delta Lake. Established robust guardrails using the combined might of AWS Lambda, Apache Airflow and Databricks, ensuring that data stored in the DataBricks Delta Lake consistently met the highest

potential opportunities and drive design wins marketing strategy ●Responsible for 3 Product lines(25 models; Power Supply Unit/Cooler/Chassis) Go To Marketing material ●Launched the world's first ATX3.0 Power Supply unit onto the market ● Establish PC DIY Guideline for Global Channel & End User (Airflow Data, Thermal performance Installation guideline, Gaming performance guideline) Product Trend Analysis and Crisis management 1. Collect market research to identify customer needs and market trends, such as future product development and improvement plans 2. Continuously big data public opinion analysis and conducting comprehensive user analysis are based

估等數據與機器學習的問題變得有趣的問題。 目前關注學習目標 nodejs、go、GCP、dataOps/MLOps、k8s/docker、ansible/cobbler(配置管理)、jenkins/gitlab-ci(ci/cd)、airflow、mlflow、nginx、redmine/testlink(需求與issume管理)、git/dvc(版本管理)、Scikit-learning/TensorFlow(機器學習/深度學習) ▲豐富的軟體產品與專案開發▲ 豐富且多

to-end deployment 並根據用戶回饋建立 retrain system - 幫助公司 app 增加 6 % 用戶參與度並獲得 70 % 用戶滿意度 - 運用 DVC 建置 model experiment tracking, dataset versioning 以及 model management 【Airflow 定期排程以及維運 data reports】 - 創建 2 個 ETL pipeline - 幫助 3 個不同部門維護 8 個 data reports - 利用 python 去處理並且整合 SQL, Google big query, AWS S3 bucket 不同

蔡智鈞 Principle Engineer Work Experience AugustPresent Principle Engineer HTC Build a source code scanning pipeline with CodeQL on Jenkins for 150+ projects. Build a packages scanning pipeline with Yarn on Jenkins for 30+ projects. Implement cache mechanism to archive 40% performance optimization for Jenkins pipeline. JuneMarch 2023 Software Engineer 國立暨南國際大學(National Chi Nan University) # Air quality IoT analytics systemLed a team in building an IoT device management and data analytics system using microservice architecture with Docker. Achieved 99% SLA and under 10 seconds data