轉職熱搜工作

1.品質計畫管理：擬定、執行並追蹤工程品質管理計畫，確保品質系統有效運作。 2.現場檢驗與督導：執行各分項工程前、中、後的檢驗作業，並記錄檢驗結果。 3.材料取樣與試驗：負責進場材料的抽驗、取樣及送審，聯繫試驗室並追蹤材料試驗報告。 4.缺失改善追蹤：針對施工品質缺失發出改善通知，督促分包商限期改善，並進行複查回饋。 5.文件檔案管理：彙整與管理所有品管紀錄、照片、自主檢查表及竣工品質文件。 6.品質會議參與：定期召開品管檢討會議，彙報品質進度與異常狀況。 — 歡迎對工作充滿熱情的你加入我們的團隊！ 期待您的加入來成為我們的一員，與公司一起攜手完成卓越的工程項目！立即投遞履歷吧！

1.施工規劃，施工計畫書撰寫，施工圖繪製與送審，施工影片，施工照片等資料整理篩選提送 2.設計變更、工期展延、爭議調處等案件辦理 3.工程網圖排定、調整與進版 4.工程執行預算編製、掌控、統計、歸納、分析、修正等 5.施工日報製作，施工各式文件製作送審 6.各式會議(簡報)資料製作，主辦及參與各式會議 7.對業主估驗，對廠商請款製作、辦理、審查、收送。 8.投標估算與投標文件製作、竣工結算與竣工文件製作。 9.移工管理、工務行政管理 10.其他主管交辦事項

1. DT/ WKS硬體電路設計，使用CAD軟體開發電路圖。 2. 進行硬體原型測試，執行功率和性能基準測試，優化設計。 3. 從設計分析確認Design Quality,處理設計相關的問題，確保產品品質。 4. 診斷硬體故障，提供技術支援。 5. 新技術研究開發與導入

1.執行大型擴建專案完成與交付及擴建專案相關之機電整合、規劃。 2.整合建廠之機電規畫設計規範與需求。 3.建立各項建廠專案管理SOP與機電設備管理準則等文件 4.具電氣/給水/空壓/消防/排水等系統施工監造，與施工廠商協調，及系統設備試機。 5.具SMT製程相關認知及產線配置圖面，及設備二次配銜接相關認知。 6.熟識AutoCAD等其他繪圖軟體。 7.協助評估改善廠務系統，提升運轉效能。 8.可配合公司國外出差（包含亞洲及美洲等地區）

1. 工廠 layout 廠區規劃 2. 規劃最佳生產流程，編寫作業指導書 3. 制定標準工時 (Standard Time) 4. 與其他單位溝通協調整合技術開發工作 5. 測試工段RFQ 報價評估 6. 須配合出差，出差時長一年約90~180天

* 負責雲端Server伺服器的相關工作 1. Component Management 2. Project BOM Management 3. Alternative component survey 4. Database Maintain • 計畫介紹： • 實習期間：即日起-2026/6 (長期實習-一學期) • 職缺內容: 實習生 (在學學生) • 實習薪資: 時薪(一週3天以上)，優於基本工資

1.系統設計開發人員 2.系統開發可行性評估、系統DFX檢查、系統排線繞線定義、定制系統需求規格書 3.精通英文、溝通協調能力

1. 與團隊進行Server機櫃機構結構設計 2. 產品開發過程與各Function team進行結構設計檢討與改善 3. 量產前需與工廠檢討量產問題分析與問題改善 4. 生產過程中能及時解決生產問題

＜About the job＞ As the Senior Data Engineer or Data Architect, you‘ll join the various advanced Data Science & AI Projects in the corporate headquarters. As well as developing intelligent applications via related AI and Big Data Analytics Technology for digital transformation, you will have plenty of opportunities to develop emerging applications based on different use cases and expand your tech skillset in this world-class company (Fortune Global 500, 20th). ＜Job Description＞ *Type 1：Senior Data Engineer Responsible for acquiring data using API, Web scraping, or other data accessing protocol/scripting and developing the ETL data pipelines, and the data aggregation systems. Using software development experience to design and build high-performance automated systems. As below: 1. Data Processing (1)Data ETL(Extracting/Transforming/Loading) process engineering and querying from relational data management(such as SQL Script). (2)Building systematic data quality processes and checks to ensure data quality and accuracy. (3)Solid coding experience in Python or Java. 2. Data Pipeline Development (1)Develops a data integration process, including creating scalable data pipelines and building out data services/data APIs. (2)Create a data processing automation and monitoring mechanism by optimizing the data pipeline process. (3)Experience with dataflow/workflow/management tools, such as Apache Nifi, Apache Airflow, Azkaban, etc., is preferred. 3. Data Crawling (1)Build scalable tools that automate web crawling, scraping, and data aggregation from various web pages using frameworks such as Scrapy. (2)Accessing data from REST APIs, particularly in parsing data in disparate formats such as JSON and XML, and developing automated engineering. (3) (Nice to Have) Knowledge of server-based front-end/UI technologies, including Vue/React and HTML/CSS, is preferred. *Type 2：Data Architect Responsible for designing, implementing, and maintaining scalable and reusable system architectures/data architectures for complex data structures and large data in data science and AI projects. As below: 1. Data Schema Design (1)Collaborate with the team to design DB/Table Schema and Data Schema. (2)Consolidate the requirements and use data engineering tech to design and implement a robust Data Mart. (3)Experience handling all kinds of structured/semi-structured/unstructured data and streaming data is preferred. (4)Hands-on experience with Dimensional Data Modeling(Column-based data warehouse) or NoSQL Schema design. 2. Data Platform Architecture (1)Design and build data infrastructure/platform components to support complex data pipelines ingesting various data from multiple internal and external data sources and processing. (2)Familiar with Big Data frameworks and processing technologies, ex: Hadoop, Apache Spark, NoSQL ...etc. (3)Familiar with AZURE or AWS cloud data services(hands-on experience with cloud infrastructure will be a plus) (4)Familiarity with the Linux OS environment, the Shell Scripting, and infrastructure knowledge. (5)(Nice to Have)Experience with declarative infrastructure/container technologies, such as Docker and Kubernetes (k8s/k3s). (Nice to Have).