Lead Data Engineer, Investment Data Platform

Castleton Tower•New York, NY

8d•Hybrid

About The Position

The Firm 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. The Opportunity We are looking for a senior, hands-on data engineering lead to help a prominent asset allocator client build a modern investment data platform. The firm has outgrown a collection of disconnected applications and spreadsheets and is building a unified data foundation across its investment, operations, and reporting functions. This is a builder's role. The right person has deep technical expertise, writes and reviews production code, and holds a high bar for quality. They know what good data engineering looks like, and when work falls short of it they fix it and help the team raise its standards. You will lead technical work across a small, growing team and partner directly with the head of the function.

Requirements

8+ years of hands-on experience in data engineering, analytics engineering, or software engineering focused on data platforms.
3+ years leading technical work, as a tech lead, principal or staff engineer, or engineering manager who still builds.
Expert SQL and Python, and deep data modeling and architecture judgment that goes beyond any single tool.
Production experience with modern warehouse and lakehouse platforms (Snowflake, Databricks, or similar), dbt, and orchestration tools (Dagster, Airflow, or similar).
Strong software engineering practice: version control, code review, automated testing, and CI/CD applied to data.
Track record of shipping and operating systems you built, described in terms of what you delivered.

Nice To Haves

Experience with investment management systems and data: security master, IBOR/ABOR, OMS, risk, performance, fund accounting, or client reporting.
Private markets data, including entity mapping across sources without universal identifiers.
Azure data services (Azure Data Factory, Azure DevOps pipelines, Synapse) and Power BI.
Experience in asset allocators, asset managers, hedge funds, private markets firms, or financial data providers.

Responsibilities

Platform architecture: Design and build the warehouse and lakehouse, data models, orchestration, and semantic layers that the investment organization runs on.
Hands-on delivery: Build production-grade Python, SQL, dbt, and orchestration work yourself, including batch and near-real-time pipelines.
Engineering standards: Bring software engineering discipline to data: code review, testing, CI/CD, documentation, observability, and data quality controls.
Technical leadership: Lead and mentor engineers through design reviews and pairing, set the technical direction for your area, and own outcomes end to end.
Business partnership: Work with investment, operations, finance, and reporting stakeholders to turn their needs into durable data products.
AI-enabled engineering: Use tools such as Claude Code, Codex, Cursor, and Copilot to move faster without compromising quality or controls.
Architect and build core investment data domains such as security and entity reference data, positions and transactions, performance, risk, and private-markets fund data.
Design data models that are correct, reconcilable, and point-in-time aware, with tests and controls that catch problems before users do.
Own reliability, performance, and cost of the platform, including monitoring, alerting, and incident follow-through.
Review others' work closely, send it back when it isn't right, and make the next version better.
Evaluate tooling and vendors across Snowflake, dbt, Dagster/Airflow, and Azure, and make pragmatic build-versus-buy calls.