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About the Role
We are seeking a senior, hands-on data engineering lead to help a prominent asset allocator client build a modern investment data platform. This is a builder's role where you will lead technical work across a small, growing team and partner directly with the head of the function.
Responsibilities
- Design and build the warehouse and lakehouse, data models, orchestration, and semantic layers.
- Build production-grade Python, SQL, dbt, and orchestration work yourself, including batch and near-real-time pipelines.
- Implement software engineering discipline to data: code review, testing, CI/CD, documentation, observability, and data quality controls.
- Lead and mentor engineers through design reviews and pairing, set technical direction, and own outcomes end to end.
- Partner with investment, operations, finance, and reporting stakeholders to turn their needs into durable data products.
- Utilize AI tools like Claude Code, Codex, Cursor, and Copilot to enhance speed and quality.
- 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.
- Own reliability, performance, and cost of the platform, including monitoring, alerting, and incident follow-through.
- Review others' work closely and provide constructive feedback.
- Evaluate tooling and vendors across Snowflake, dbt, Dagster/Airflow, and Azure, and make pragmatic build-versus-buy decisions.
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 skills.
- Deep data modeling and architecture judgment.
- Production experience with modern warehouse and lakehouse platforms (Snowflake, Databricks, or similar).
- Production experience with dbt.
- Production experience with orchestration tools (Dagster, Airflow, or similar).
- Strong software engineering practices: version control, code review, automated testing, and CI/CD applied to data.
- Track record of shipping and operating systems you built.
- Experience with investment management systems and data (security master, IBOR/ABOR, OMS, risk, performance, fund accounting, or client reporting) is strongly valued.
- Experience with private markets data, including entity mapping across sources without universal identifiers, is strongly valued.
- Experience with Azure data services (Azure Data Factory, Azure DevOps pipelines, Synapse) and Power BI is strongly valued.
- Experience in asset allocators, asset managers, hedge funds, private markets firms, or financial data providers is strongly valued.
- A craftsman mindset with attention to detail.
- High standards for yourself and others, delivered constructively.
- Ownership mentality, seeing problems through to resolution.
- Clear communication skills with both engineers and investment professionals.
Skills
- Python
- SQL
- dbt
- Snowflake
- Databricks
- Dagster
- Airflow
- Azure Data Factory
- Azure DevOps pipelines
- Synapse
- Power BI
- Data Modeling
- Data Architecture
- Orchestration
- CI/CD
- Code Review
- Automated Testing
- Observability
- Data Quality Controls
Location
- Hybrid, Northeast U.S.
Work Type
- Hybrid
- Full-time
Experience Level
- Senior
- Lead
Salary/Compensations
- Competitive total compensation commensurate with experience.
About the Company
- 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.