Data Engineering Lead (Senior Vice President)

Join iCapital as a Data Engineering Senior Vice President, where you will lead the evolution of the enterprise data platform that powers AI, automation, analytics, and data-driven decision making. You will define and execute the architectural vision, roadmap, and operating model for a modern, AI-ready data ecosystem. This role requires extensive experience in data engineering, cloud technologies, and financial services.

• Defining and executing the architectural vision, roadmap, and operating model for a modern, AI-ready data ecosystem.
• Establishing standards for data engineering, governance, security, observability, quality, and documentation, and owning platform reliability.
• Building and leading a high-performing Data Engineering organization and culture, and scaling data engineering organizations through periods of rapid growth.

• Financial services experience within asset management, wealth management, fintech, banking, or insurance, including knowledge of regulated data environments and governance requirements
• Experience building AI- and ML-enabled data platforms, including semantic layers, feature stores, model-serving infrastructure, or LLM-integrated architectures
• Experience with Informatica or comparable enterprise data catalog, metadata management, and MDM platforms
• Hands-on experience with analytics engineering, semantic layer development, and modern data transformation frameworks such as dbt
• Experience partnering with and presenting to executive stakeholders within regulated industries, translating complex technical concepts into business impact
• Proven track record designing, building, and operating enterprise-scale cloud data platforms (Snowflake, Databricks, or equivalent), including architecture, security, governance, and cost optimization
• Proven success scaling data engineering organizations through periods of rapid growth, including organizational design, hiring, and leadership development
• 12+ years of experience in data engineering, including 5+ years leading large-scale engineering organizations across multiple functions
• Demonstrated success developing multi-year platform strategies, securing investment, prioritizing initiatives, and delivering measurable business outcomes
• Deep expertise establishing data engineering best practices across pipeline design, data quality, observability, data contracts, documentation, and operational support
• Knowledge of alternative investments, wealth management, fund administration, or related financial services domains
• Deep expertise in Snowflake architecture, performance optimization, security, and data sharing at enterprise scale