Senior Back-end Python Developer (ETL focus)

薪資範圍:100,000 ~ 120,000 TWD / month

公司名稱: ZeNPulsar Ltd

Main fields of development are :

  • Classify posts and accounts from 12 sm/fintech platforms covered in order to get nuanced sentiment (e.g. bullish bots or long-short FOMO) towards more than 20k assets (stocks, etf, crypto)
  • Classify accounts by their professionalism and stance to certain market strategies

Main fields of development:

  • Core: infrastructure, architecture and API
  • Data: work together with data scientist on financial market data time-series
  • Updating of the infrastructure servicing
  • New solution prototyping
  • Active participation in the company life (e.g. new ideas, pet projects, opensource) is much appreciated

Back-end:

  • Python (aiohttp / FastApi /Pydantic )
  • Queues in RabbitMQ (Redis is a plus)
  • RESTful API (Swagger)
  • Microservice architecture

Data management and ETL

  • Docker
    • Kubernetes (is a huge plus, general understanding needed)
    • AirFlow
    • Google Cloud

Understanding of Chinese social media platforms, especially:

  • WeChat
  • Weibo
  • Zhihu

公司地址:

20-22 Wenlock Road, London, UK

其他:

AI powered SaaS Forensic Solution to dominate disinformation, advance online integrity and digital trust.We defend businesses against the increasing threat of online disinformationby detecting & predicting social media manipulation.Our first solution dedicated to financial services Pump by ZeNPulsar delivers a unique threat intelligence analysis and monitoring solution of engineered narratives based on an exclusive combination of data science and AI analytical methods fine-tuned specifically for this purpose.ZeNPulsar currently analyzes sources including 12 social media platforms (such as Facebook, Gab, Reddit, Telegram, Twitter, and YouTube) and pricing and market data for thousands of securities, including ETF and crypto assets in real time. As the models and datasets created by ZeNPulsar are sharply focused on market manipulation, and not broad social listening paradigms, they provide much-needed forecasting capabilities.-2024-11-19
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