data Work Experience Vpon, Data Engineer Aug 2018 ~ Oct 2020 Implement Akka-http(Scala) server endpoints for Vpon Data Platform Product Create new ETL pipelines using GCP Spark(Apache Spark) and GCP Dataflow(Apache Beam) to batch input/output hundreds of files Migrate existing ETL pipelines from AWS(Hive SQL) to GCP(BigQuery SQL) using python Migrate datawarehouse from AWS(Hive) to GCP(BigQuery) Setup Prometheus on GKE (Google Kubernetes Engine) to monitor resource usage(CPU, memory of compute engine) Largitdata, Data Quality Manager Sep 2017 ~ July 2018 Monitor status of all web scraping running scripts.( Flask ) Write

Chin-Hung (Wilson) Liu I am a lead architect responsible for designing and implementing a large-scale data pipeline for Lomotif, Paktor x 17LIVE, utilizing GCP/AWS/Python/Scala, in collaboration with data science and machine learning teams in Singapore and TW HQ, as well as with the Hadoop ecosystem (HDFS/HBase/Kafka) at JSpectrum in Hong Kong and Sydney. With over 15 years of experience in designing and developing Java/Scala/Python-based applications for daily operations, I bring: ● At least 8 years of experience in data analysis, pipeline design

Wan-Ling (Lyn), Chen CAREER SUMMARY 7 years of diverse data and AI experience in engineering and advisory roles across varied industries. Provided end-to-end advisory on data and AI solutions, covering data platforms, lakehouse architecture, data security & LLM. Led PoCs delivery, solution design, and workshop execution for clients' digital transformation. Managed data architecture across 70 global offices, demonstrating adaptability in complex data landscapes. Actively participated in projects addressing board portfolios such as data and AI, security, and automation. Achieved a 76.6% revenue boost through strategic volume monetization. Goal-oriented, curious

Taiwan and China (supply chain, information security, provision for slow-moving items, food safety, reimbursement etc.) - Design to execution of risk analysis, test of controls, and test of details; outperformed peers in abilities dealing with data and delivering results to company executives Staff Auditor • Deloitte SeptFebAuditing of Taiwanese companies based in China in both PRC and Taiwan IFRS; mainly manufacturing industry and for consolidation of entities. - Outperformed peers in resilience, took charge of small case audits, performed well under pressure and working independently Academic BackgroundNational Taiwan University Accounting Skills Excel SQL ACL Project Management Languages English — 專業

— Jingping Wang 王景平 More than 15+ years of experience in software development. Projects participated include from large scale insurance/banking web applications to mobile applications. In recent years, I have been engaged in software defined networking and 4G/5G user data plane networking. Taipei City, Taiwan Phone:E-Mail: jingping.w＠tcs.com EXPERIENCE Full Stack Engineer • TATA Consultancy Services (TCS) JanPresent TaiShin Bank Richart App Frontend / 台新 Richart 數位銀行 TaiShin Bank Richart App Backend / 台新 Richart 數位銀行 TaiShin

李信憲 Jeffrey Data Engineer As a data engineer, I have almost 5 years of experience in data engineering, data processing, and data modeling. Also building a hybrid cloud data pipeline with high reliability and high availability to do ETL including change data capture (CDC). Taipei City, Taiwan [email protected] Python PySpark / NiFi Kafka / Debezium Apicurio SQL / ClickHouse / MinIO Docker / Docker compose Google Cloud Platform (GCP) Bash Shell / Linux Git / GitLab Work Experience Data Engineer JKOPay Co. Ltd. • MayPresent ETL & Data Sync Dockerized a PySpa

perseverance in face of challenging problems; Continue absorbing “technical-nutrients” from the development of open-source AI community. PROJECT Next-Generation ETL Framework for Data Science Team 2022/7-2022/12 Goal: Design a lightweight ETL python framework enabling data scientists to seamlessly deploy their pandas/SQL processes to airflow in a maintainable format. Reduce the maintenance and optimization cost of AI projects with integration of customizable data validation, metadata registration, and time/memory-profiling tools. Ensure flexibility and extensibility in pandas-like packages (such as CuDF, Polars, PySpark, etc) for future parallelizability