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Data Engineering Lead

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Onsite

Data Engineering Lead

iCapital

Data Engineering Lead

Skills

Data EngineeringSnowflakeDatabricksData AnalysisMachine LearningAIdbt

1 month ago

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Important information


Salary:

$200,000 – $250,000 / year

Location:

New York, NY, USA

Work mode:

Onsite

Published on:

30 September 2026

What they need


Who you are

  • 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

What the job involves

  • 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
  • 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

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