Director / Senior Director, AI & Data Engineering

Castleton Tower•New York, NY

3d•Hybrid

About The Position

Castleton Tower is a boutique consulting firm founded by executives who have built and led quantitative research, data science, and technology teams at top-tier hedge funds and asset managers. We work exclusively with investment management firms, including asset allocators, asset managers, hedge funds, family offices, and RIAs, helping them modernize data infrastructure and build AI-ready foundations. Our engagements combine senior strategy with hands-on implementation. We assess technical and business strategy, design the architecture, and build the data and AI infrastructure needed to support better investment decisions. We are looking for a senior data and AI leader to design, build, and ultimately manage a modern data engineering function for a prominent asset allocator client. This is a leadership role for someone who can operate from first principles: define the platform strategy, build production systems, hire and develop talent, and work directly with senior investment and operating stakeholders. This is not a narrow individual-contributor engineering role. The right person can still get close to the code, but their broader mandate is to turn fragmented data, tooling, and process into a scalable operating model for an investment organization.

Requirements

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.

Nice To Haves

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.
Builder-manager mindset: able to set direction, manage people, and still understand the technical details.
Pragmatic systems thinker who can connect architecture, process, talent, and business outcomes.
High standards for data quality, reliability, documentation, and maintainability.
Comfortable challenging assumptions while staying collaborative with senior stakeholders.
Motivated by the opportunity to build a durable data function inside a sophisticated investment organization.

Responsibilities

Data team design: Define the target operating model, roles, roadmap, standards, and ways of working for a high-performing data engineering and analytics team.
Platform architecture: Design the data warehouse/lakehouse, semantic layers, orchestration, governance, quality controls, and analytics delivery patterns.
Hands-on delivery: Build and review production-grade Python, SQL, dbt, orchestration, and cloud infrastructure work where needed.
AI-enabled engineering: Use modern AI development tooling, including tools such as Claude Code, OpenAI Codex, Cursor, GitHub Copilot, and Databricks AI capabilities, to accelerate delivery without compromising quality, controls, or maintainability.
Stakeholder partnership: Translate needs from CIOs, PMs, COOs, operations teams, and finance stakeholders into durable data products and operating processes.
People leadership: Hire, coach, and manage engineers and analytics talent; establish review practices, delivery rituals, technical standards, and performance expectations.
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.