Data Engineering Lead - Senior Vice President

About the Role

iCapital is looking to hire a Data Engineering Senior Vice President, to lead the evolution of the enterprise data platform that powers AI, automation, analytics, and data-driven decision making across the firm while positioning proprietary data as a strategic business asset. This role is responsible for defining and executing the architectural vision, roadmap, and operating model for a modern, AI-ready data ecosystem built on a strong foundation of cloud technologies and established engineering capabilities.

The position will provide leadership across platform architecture, engineering standards, and cross-functional alignment while overseeing the development of scalable data products, semantic layer capabilities, and future AI enablement initiatives. Partnering closely with an engaged executive steering committee, this individual will play a critical role in shaping how data supports the firm's long-term growth and innovation strategy.

Responsibilities

• Define and own iCapital's enterprise data architecture, including the Snowflake platform, semantic layer, reusable data products, and AI-ready ecosystem.
• Establish standards for data engineering, governance, security, observability, quality, and documentation.
• Own platform reliability, SLAs, DataOps practices, and incident management.
• Lead architectural strategy, tooling decisions, and platform investment recommendations.
• Develop and execute the multi-year Data Engineering roadmap aligned to business and AI priorities.
• Translate enterprise data strategy into prioritized engineering initiatives and platform capabilities.
• Build business cases for platform investments and proactively address technical debt and future scalability needs.
• Partner with business and technology leaders to deliver data products that support enterprise objectives.
• Communicate platform progress, risks, and investment decisions to executive stakeholders.
• Build and lead a high-performing Data Engineering organization and culture.

Qualifications

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