Nextmind Solutions Private Limited
Asset Management - Senior Data Engineer - VP
- Compensation
- Not disclosed
- Location
- Shanghai, China
- Work setup
- Not specified
- Seniority
- C level
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Education: Bachelor’s degree or higher in Computer Science, Data Science, Artificial Intelligence (AI), Machine Learning (ML), or related fields.
- Experience: Minimum of 8+ years of data engineering experience, with at least 5 years in a leadership or senior engineering role.
- Technical Expertise: Expert-level proficiency in SQL and experience with relational databases (e.g., Oracle, PostgreSQL) and MPP data warehouses (e.g., Redshift, Snowflake). Strong hands-on experience
- Leadership & Team Development: Demonstrated ability to lead and mentor engineering teams, drive technical excellence, and foster a culture of innovation.
- Business Acumen: Strong understanding of fund management and investment data domains, with the ability to translate business needs into technical solutions.
- Soft Skills: Excellent communication, cross-functional collaboration, and problem-solving skills.
- Preferred Qualifications: Experience in leading data platform development for financial services or asset management. Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization (e.g
Full description
Job Summary:
We are seeking aSenior Data Engineer, VP to lead the design, development, and maintenance of our core data platform, with a strong focus on fund management business logic, platform-level engineering, and AI/ML integration. This is a senior leadership role that requires a deep understanding of data architecture, distributed systems, and the ability to shape the technical direction of the data and AI functions within the firm. The ideal candidate will be a strategic thinker, a hands-on engineer, and a leader who can drive innovation and deliver high-impact results.
Job Responsibilities:
- Lead the design, development, and maintenance of the company’s core data platform, ensuring scalability, reliability, and alignment with business needs.
- Drive the implementation of data infrastructure that supports fund management, investment analytics, and AI/ML integration.
- Collaborate with business and technology stakeholders to define data requirements, design solutions, and deliver high-quality systems.
- Architect and optimize data pipelines, ETL/ELT workflows, and real-time streaming systems (e.g., Kafka, Flink, Spark).
- Promote the adoption of cutting-edge data and AI technologies, continuously enhancing our platform capabilities and business value.
- Formulate technical strategies and lead the resolution of complex engineering challenges, ensuring high-quality delivery.
- Oversee project execution, including requirement gathering, system design, development, testing, and deployment, while managing risks and ensuring quality.
- Lead the development of system specifications and ensure compliance with internal standards, including code reviews and third-party integration.
- Champion the integration of AI/ML into data platforms, enabling predictive analytics, automation, and smarter client engagement.
Qualifications and Requirements
- Education: Bachelor’s degree or higher in Computer Science, Data Science, Artificial Intelligence (AI), Machine Learning (ML), or related fields.
- Experience: Minimum of 8+ years of data engineering experience, with at least 5 years in a leadership or senior engineering role.
- Technical Expertise:
- Expert-level proficiency in SQL and experience with relational databases (e.g., Oracle, PostgreSQL) and MPP data warehouses (e.g., Redshift, Snowflake).
- Strong hands-on experience with big data technologies (e.g., Apache Spark, Hadoop, Kafka, Flink).
- Proven experience in designing and optimizing ETL/ELT workflows, data pipelines, and real-time processing systems.
- Deep knowledge of data modeling, data architecture, and data governance principles.
- Strong background in AI/ML integration, including experience in deploying models, building data pipelines for machine learning, and leveraging data for AI-driven insights.