Senior Analyst, Data Engineering & Investment Analytics

Castleton Tower•San Francisco, CA

1d•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 (asset allocators, asset managers, hedge funds, family offices, and RIAs) 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 code up the full-stack infrastructure where needed. The Opportunity We're looking for someone who lives at the intersection of data engineering and investment analytics. You'll be equally comfortable building a production data pipeline in Python and presenting portfolio analytics insights to a CIO. This role blends hands-on technical work with strategic client engagement — you'll build the data infrastructure and derive the business insights that drive investment decisions. This is not a pure engineering role or a pure strategy role. It's both. You'll design Snowflake schemas in the morning, build dbt models after lunch, and walk a client through their portfolio risk dashboard before the end of day. What you'll get: Strategic Impact: Lead full-scope projects from architecture design through analytics delivery — not just strategy decks or just code Direct Mentorship: Work alongside the firm's Principals on every engagement Modern Tech Stack: Work with best-in-class tools (Snowflake, Databricks, dbt, Dagster, Sigma, AWS, etc.) and cutting-edge AI/agentic development workflows Path to Growth: Clear trajectory toward expanded responsibilities, with potential to transition into a senior role at a leading Bay Area asset allocator

Requirements

5+ years of hands-on experience in a role that combined data/analytics engineering with business analysis or investment analytics
Prior experience within investment management, financial services, or firms that serve them (hedge funds, asset managers, fintech, financial consulting)
Proficiency in Python and advanced SQL for both data engineering and analytical work
Experience with modern data platforms (Snowflake, Databricks, or similar) and data modeling principles
Demonstrated ability to communicate technical concepts to non-technical investment professionals
Comfort working directly with senior clients and managing stakeholder expectations

Nice To Haves

Experience with data pipeline orchestration tools (Airflow, Dagster, Prefect)
Background in portfolio analytics, fund accounting, or investment operations workflows
Familiarity with BI/visualization tools (Sigma, Tableau, Looker)
Management consulting experience (strategy or implementation)
Experience building quantitative trading or investment tools
High ownership mentality: you see problems through to resolution without being told
Equally curious about the data engineering and the investment side
Comfort with ambiguity and ability to structure unstructured problems
Executive presence to hold your own with senior investment leaders
Independent thinker who takes initiative and understands what high-quality work looks like

Responsibilities

Investment Analytics & Client Engagement: Partner with investment professionals (PMs, CIOs, COOs, Heads of Operations) to understand their analytical needs and translate them into data products
Build customized dashboards, analytics tools, and quantitative investment applications
Present data-driven insights and recommendations to senior client stakeholders
Identify opportunities where better data infrastructure can improve investment processes
Data Engineering & Technical Delivery: Design and implement scalable data platforms (data warehouses, data lakes) and end-to-end data pipelines for investment firms
Develop and optimize production-grade code (Python/SQL) for data transformation and financial analysis
Build and maintain data models that support portfolio analytics, risk reporting, and investment operations
Leverage AI-assisted development tools to accelerate delivery
Bridging the Gap: Translate business requirements from investment teams into robust technical architectures
Own projects end-to-end: from scoping the business problem, to building the data layer, to delivering the analytics
Ensure technical solutions are grounded in real investment workflows, not just technically elegant