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Data Engineer
at Numus
5 months ago | 436 views | 3 applications

Data Engineer

Per year
$125,000 To $200,000

About the company

Numeus is a diversified digital asset investment firm built to the highest institutional standards, combining synergistic businesses across Trading, Alpha Strategies, Asset Management, and Venture Capital . Numeus was founded by successful executives with decades of experience across the finance, blockchain and technology industries, with a shared passion for digital assets. Our values are grounded in an open approach based on connectivity, collaboration, and partnerships across the digital asset ecosystem. People and technology are at the core of everything we do.

Job Summary

Key Responsibilities:

📍Work closely with quantitative researchers to understand their data requirements, provide technical solutions, and integrate data engineering processes effectively with quantitative strategies 📍Design, develop, and optimize robust data pipelines, enabling ETL processes that support quantitative research, analysis, alpha forecasting, and execution 📍Implement rigorous data quality assurance measures to ensure the accuracy, reliability, and consistency of data inputs and outputs 📍Evaluate, select, and manage the appropriate data engineering tools, technologies, and infrastructure, with a focus on scalability and automation 📍Collaborate with compliance and risk management teams to ensure data privacy, security, and regulatory compliance standards are adhered to 📍Effectively communicate complex technical concepts to non-technical stakeholders, enabling them to understand the value and impact of specific data engineering initiatives

Skillset and Qualifications:

📍3+ years experience in data engineering. Previous experience at a quantitative hedge fund is highly desirable. 📍Master's or Ph.D. in Computer Science, Engineering, Data Science, or a related field 📍Proficiency in programming languages such as Python and C++, along with experience in big data technologies. Strong knowledge of SQL and relational databases. 📍Demonstrated expertise in designing and optimizing data pipelines, data modeling, ETL processes, data warehousing, and data governance 📍Proven ability to collaborate effectively with quantitative researchers and other stakeholders, translating their requirements into technical solutions 📍Strong analytical and problem-solving skills, capable of analyzing complex datasets, identifying patterns, and extracting meaningful insights 📍Thrive in a fast-paced and dynamic environment, adaptable to changing priorities and emerging technologies 📍Strong verbal and written communication skills

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