Castleton Tower - Senior Consultant (Data Engineering, Investment Analytics, Strategy)
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. • Strongly Valued • Strongly Valued • 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. • Personal Attributes • Personal Attributes • 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. • Location and Placement • Location: Hybrid, Northeast U.S. • This role is intended for placement at a prominent asset allocator client. The successful candidate should be comfortable working closely with senior investment, operations, and technology leaders and spending regular time in person as needed. • Compensation: Competitive total compensation commensurate with experience.
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. • Data team design: • Platform architecture: Design the data warehouse/lakehouse, semantic layers, orchestration, governance, quality controls, and analytics delivery patterns. • Platform architecture: • Hands-on delivery: Build and review production-grade Python, SQL, dbt, orchestration, and cloud infrastructure work where needed. • Hands-on delivery: • 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. • AI-enabled engineering: • Stakeholder partnership: Translate needs from CIOs, PMs, COOs, operations teams, and finance stakeholders into durable data products and operating processes. • Stakeholder partnership: • People leadership: Hire, coach, and manage engineers and analytics talent; establish review practices, delivery rituals, technical standards, and performance expectations. • People leadership: • 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. • Data Platform and Analytics 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. • AI and Modern Engineering Workflows • 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.
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