HybridHybrid in the NYC area, with roughly one week of monthly travel for on-site client work.
Data & AnalyticsData Engineering
About the job
Requirements
At least 7 years of hands-on experience in a technical or analytical role such as Quant, Quant Developer, Data Scientist, or Data Engineer; highly exceptional candidates may qualify with at least 5 years.
Prior experience at hedge funds, fintech firms or startups, market makers, or top-tier asset management firms; experience at top technology or strategy consulting firms is considered case by case.
Demonstrated project leadership, ideally overseeing technical delivery from inception to production.
Proficiency in Python, including Pandas, NumPy, and Scikit-learn, and advanced SQL query optimization.
Experience with at least one major cloud platform—AWS, GCP, or Azure—and associated data services such as Snowflake or Databricks.
Deep understanding of data modeling and data warehousing principles.
Ownership mentality and ability to see problems through to resolution independently.
Ability to recognize and deliver high-quality work.
Curiosity about new technologies and the investment industry.
Ability to understand broader business context in addition to technical requirements.
Independent thinking and initiative.
Responsibilities
Lead the design and implementation of scalable data platforms, including data warehouses, data lakes, and end-to-end data pipelines.
Develop and optimize production-grade code in Python and Spark for data transformation and financial analysis.
Build customized analytical applications, dashboards, and quantitative investment tools.
Ensure efficient delivery and adherence to data governance and integrity standards.
Collaborate with client investment teams to translate investment process requirements into robust, automated tools and data products.
Provide technical and project leadership across engagements.
Understand client business context to ensure technical solutions solve real problems.
Lead full-scope projects from architecture design through production deployment.
Architect data warehouses, build production pipelines, and develop quantitative tools that support investment decisions.
Desired Qualifications
Familiarity with data pipeline orchestration tools such as Airflow, Dagster, or Prefect.
Experience building quantitative trading or investment tools.
Background in financial data, including market data, portfolio analytics, or fund accounting.
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 (RIAs, family offices, hedge funds, and asset allocators) helping them modernize their data infrastructure and build AI-ready foundations.
What makes us different: We're a lean firm where senior practitioners do the actual work. No armies of junior consultants learning on your dime. Our engagements blend high-level strategy with hands-on technical implementation. We'll assess your technical & business strategy, design your data architecture and also code up the full stack infrastructure where needed.
The Opportunity
We're looking for a technical leader who can design and build data platforms for investment firms. This role emphasizes hands-on engineering while maintaining strategic client engagement. You'll architect data warehouses, build production pipelines, and develop quantitative tools that directly support investment decisions.
What you'll get:
Strategic Impact: Lead full-scope projects from architecture design to production deployment
Direct Mentorship: Work alongside the firm's Principals on every engagement
Modern Tech Stack: Work with best-in-class tools (Snowflake, Databricks, dbt, Dagster, cloud infrastructure, etc.) and cutting-edge AI/agentic development workflows
Path to Partnership: Clear trajectory toward firm equity and partnership for high performers
Core Responsibilities
Technical Leadership
Lead the design and implementation of scalable data platforms (data warehouses, data lakes) and end-to-end data pipelines
Develop and optimize production-grade code (Python/Spark) for data transformation and financial analysis
Build customized analytical applications, dashboards, and quantitative investment tools
Ensure efficient delivery and adherence to data governance and integrity standards
Client Engagement
Collaborate with client investment teams to translate investment process requirements into robust, automated tools and data products
Provide technical and project leadership across engagements
Understand client business context to ensure technical solutions solve real problems
Qualifications
Required:
7+ years of hands-on experience in a technical or analytical role (Quant, Quant Dev, Data Scientist, or Data Engineer). Minimum of 5 years for highly exceptional candidates
Prior experience at hedge funds, fintech firms/startups, market makers, or top-tier asset management firms. Experience at top technology or strategy consulting firms considered on a case-by-case basis
Demonstrated project leadership, ideally overseeing technical delivery from inception to production
Proficiency in Python (Pandas, NumPy, Scikit-learn) and advanced SQL query optimization
Experience with at least one major cloud platform (AWS, GCP, or Azure) and associated data services (Snowflake, Databricks)
Deep understanding of data modeling and data warehousing principles
Valued:
Familiarity with data pipeline orchestration tools (Airflow, Dagster, Prefect)
Experience building quantitative trading or investment tools
Background in financial data (market data, portfolio analytics, fund accounting)
Personal Attributes:
High ownership mentality: you see problems through to resolution without being told
Understands what high-quality work looks like and how to deliver it
Curious and interested in learning about new technologies and the investment industry
Seeks to understand the broader business context, not just the technical requirements
Independent thinker who takes initiative
Location & Compensation
Location: Hybrid in the NYC area. Expect to travel roughly one week per month for on-site client work.
Compensation:
Total expected compensation: $250 to $500K+ (commensurate with experience)