Director / Senior Director, AI & Data Engineering

Core Responsibilities

• Leadership and Operating Model Design and Implement the data team structure, hiring plan, delivery model, and long-term technical roadmap.
• Set engineering standards for code review, testing, documentation, observability, data quality, and production support.
• Manage internal team members, contractors, vendors, and implementation partners where appropriate.
• Build a culture of ownership, technical rigor, and pragmatic delivery.
• Architect and build scalable data platforms across warehouse, lakehouse, orchestration, transformation, and BI layers.
• Develop data models and applications that support portfolio analytics, investment operations, risk reporting, finance, and executive reporting.
• Create durable pipelines and controls for high-value investment and operational datasets.
• Evaluate and rationalize tooling across Snowflake, Databricks, dbt, Dagster/Airflow, cloud infrastructure, BI, and internal applications.
• Use AI coding assistants and agentic workflows to accelerate software and data delivery while maintaining security, review, and testing discipline.
• Identify high-leverage AI use cases across data ingestion, documentation, analytics, workflow automation, and research operations.
• Design human-in-the-loop processes for AI-generated code, analysis, and operational outputs.
• Help the organization build the data and governance foundation required for responsible AI adoption.

Qualifications

• 10+ years of progressive experience in data engineering, analytics engineering, data platforms, or technical data leadership.
• 3+ years managing engineers, analytics engineers, data platform teams, or cross-functional technical delivery teams.
• Proven ability to design and build production data platforms using Python, SQL, modern warehouse/lakehouse technologies, and orchestration tools.
• Strong architectural judgment across data modeling, governance, quality, observability, security, and operational resilience.
• Practical fluency with AI-assisted development tools such as Claude Code, OpenAI Codex, Cursor, GitHub Copilot, or similar systems.
• Ability to communicate clearly with senior non-technical stakeholders and translate business needs into durable technical systems.
• Executive presence, high ownership, and comfort operating in ambiguous environments.
• Experience in investment management, asset allocation, hedge funds, private markets, family offices, RIAs, fintech, or financial data products.
• Experience building or modernizing data teams in a high-expectation investment, finance, or institutional environment.
• Hands-on exposure to Snowflake, Databricks, dbt, Dagster, Airflow, AWS, Azure, Sigma, Tableau, Looker, or similar tooling.
• Experience with portfolio analytics, manager research, risk reporting, investment operations, fund accounting, or performance reporting datasets.
• Experience evaluating vendors and implementation partners, including build-versus-buy decisions.