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Big Data Engineer

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Job updated alrededor de 2 años ago

Descripción del trabajo

Ephod Technology is a FinTech startup focus on helping global money managers to make better decisions with the help of quantitative analysis.The investment research industry is heavily manpower dominated. Industry research reports, company financial models and trading strategies are mostly analyzed by human. In EphodTech, we work together as a team to disrupt the industry to modernize the decision making process of money management.

We are now working with fund managers in Hong Kong, Mainland China, and Taiwan for building our MVP product. We are now building a core engineering team to accelerate our development process.

You are expected but not limited to provide innovative solutions for distinct data pipeline automation, cleansing, validations and processing. We embrace people with distinct backgrounds to enrich our views on finance and technology.

Requisitos

  • Bachelor or higher degree from a leading university in a highly analytical field (i.e. Computer Science, Financial Engineering, Mathematics, Electrical Engineering, Physics)
  • Have a strong interest in learning about worldwide financial markets in a quantitative way
  • 3+ year of solid api development
  • 2+ year of any cloud infrastructure development
  • 1+ year of development or maintenance experience on any form of data pipeline in production
  • Knowledge of statistical and data processing frameworks (e.g. Python, R, Pandas)
  • Experience with distributed data tools(e.g. Presto, Hadoop, Hive, Spark,) is a plus
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800,000 ~ 1,500,000 TWD / año
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Sobre nosotros

Ephod Technology is a FinTech startup focus on helping global money managers to make better decisions with the help of quantitative analysis.

Ephod Intelligence is a Software as a Service (SaaS) platform designed to assist fundamental research analysts in validating their investment ideas and keeping a close watch on the equity market. By incorporating analysts' unique investment strategies, Ephod employs sophisticated, non-linear quantitative models to identify and recommend the most effective stock screening factors. This approach allows for the generation of personalized trading signals based on the proposed factors.




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