Asset Management - Senior Data Engineer - VP

Job Summary:
We are seeking a Senior 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.
Experience in Data Mesh build up is a strong plus
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., Docker, Kubernetes).
Fluency in English (written and spoken), with the ability to collaborate with global teams.