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1. 設計/開發/維護大數據相關專案, 包含Data Warehouse管理, 負責資料建模、收集、存取與儲存等功能 2. 建立 Batch/Streaming ETL pipeline，並確保數據品質與正確性 4. 維護雲端大數據架構及服務 5. 理解使用者

【工作內容】 與產品團隊和前後端工程團隊合作，確保資料品質與產出 創建與維護Data Infrastructure Data Pipeline & ETL/ELT Data Product開發與維護

工作職責： 負責推薦系統和廣告變現相關的數據支持，包括但不限於數據報表平台、數據分析平台等 負責數據的自動化分析處理工作，對巨量數據進行挖掘，產出數據分布規律、變化趨勢、關聯關系

如果你還會金融業抱持本土金融保守的想法，是時候該改變對金融業的看法了! 有種企圖想要把新創的氛圍帶入金融業，透過技術與數據說話。恭喜您! 與您抱持同樣想法，國內外名校，想法開放的

Handshakes by DC Frontiers is an award-winning DataTech company that leverages data to empower safe, informed business decisions. We are located in 7 cities and are expanding our presence across the globe. We are hiring an experienced Data Engineer to be based in our Singapore office. The Data Engineer will be part of our Data and AI team that is involved in building innovative products on cutting-edge technologies to solve business problems. As a

We are looking for a highly knowledgeable and enthusiastic machine learning engineer to help solve complex problems across a variety of audio processing projects. You will be both creating new solutions and evaluating existing machine learning (ML) processes, as well as performing statistical analysis to create and analyse large data sets. To ensure success as a machine learning engineer, you should be able to demonstrate solid data science knowledge and experience in a related ML role, as well

1. 行銷議題分析與行銷解決方案之建議，提供行銷數據分析報告 2. 數據分析、挖掘、建模、模式評估等相關工作，並進行模型評估 3. 與使用者及開發人員合作，定義 Report 及 Business Intelligence 需求及規格，參與

We are looking for Senior Data Engineer to join our hedge fund and manage our data operations. You will be responsible for engaging with various data vendors, designing and implementing data pipelines, and ensuring data quality and security. You will also mentor other data engineers who support the data infrastructure and pipelines of the fund. Responsibilities: Engage with various data vendors to find the best data for the fund’s investment and trading activities Evaluate data quality

1.負責雲端數據平台之架構規劃與專案導入建置 2.負責雲端數據平台之數據流程、管理機制、開發架構與平台維運

- Develop data records management processes and policies - Identify areas to increase efficiency and automation of processes - Set up and maintain automated data processes - Identify, evaluate and implement external services and tools to support data validation and cleansing - Produce and track key performance indicators of all digital channels (paid, owned, earned media) - Creating dashboards, data visualizations, and campaign and website performance reports. - Monitor and audit data quality -