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 data engineer to join a prominent asset allocator client as it builds a modern investment data platform. You will work on a small, high-standards team that is replacing a collection of disconnected applications and spreadsheets with a unified data foundation for investment, operations, and reporting. This is a hands-on engineering role for someone early in their career who wants to learn from experienced builders, take real ownership, and grow quickly. We care more about how you think, how carefully you work, and how much you want to own than about any specific tool.

Requirements

3 to 5 years of hands-on experience in data engineering, analytics engineering, or a closely related software role.
Strong SQL and Python, and solid grounding in data modeling and ETL/ELT.
Experience with a modern data warehouse (Snowflake, Synapse, Databricks, or similar) and a transformation or orchestration tool (dbt, ADF, Airflow, Dagster, or similar).
Habits of a careful engineer: version control, code review, testing, and monitoring.
Strong analytical ability and attention to detail.

Nice To Haves

Exposure to financial, investment, or private equity data.
Azure data services, Azure DevOps pipelines, or Power BI.
Experience reconciling data across systems.

Responsibilities

Build and maintain data pipelines and transformations in SQL and Python, using dbt, Snowflake, and orchestration tools such as Dagster, Airflow, or Azure Data Factory.
Model investment and operational data (positions, transactions, performance, reference data, and private-markets fund data) so it is accurate and easy to use.
Write tests, data quality checks, and reconciliations that prove the numbers are right, and fix the root cause when they aren't.
Ship through code review and CI/CD, and help keep production pipelines monitored and reliable.
Work with analysts and investment teams to understand what they need, and deliver datasets and reports (including Power BI) they can trust.
Use AI-assisted development tools to work faster while keeping quality high.