Castleton Tower Consulting, LLC

Data Engineer

• Design and build the warehouse and lakehouse, data models, orchestration, and semantic layers for the investment organization
• Build production-grade Python, SQL, dbt, and orchestration solutions, including batch and near-real-time pipelines
• Establish 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 create durable data products
• Use Claude Code, Codex, Cursor, and Copilot to accelerate engineering while maintaining quality and controls
• Architect and build investment data domains including security and entity reference data, positions and transactions, performance, risk, and private-markets fund data
• Design correct, reconcilable, point-in-time-aware data models with tests and controls
• Own platform reliability, performance, and cost, including monitoring, alerting, and incident follow-through
• Review engineering work and improve subsequent versions
• Evaluate Snowflake, dbt, Dagster/Airflow, and Azure tooling and make build-versus-buy decisions
• Work closely with the client’s engineering leadership and investment, operations, and technology stakeholders
• Spend regular time in the office and approximately one week per month on-site with the client

Lead Data Engineer, Investment Data Platform

• Build and maintain data pipelines and transformations in SQL and Python using dbt, Snowflake, and orchestration tools
• Model investment and operational data, including positions, transactions, performance, reference data, and private-markets fund data
• Write tests, data quality checks, and reconciliations to validate data accuracy
• Fix root causes of data issues
• Ship through code review and CI/CD
• Monitor and maintain reliable production pipelines
• Work with analysts and investment teams to understand requirements
• Deliver trusted datasets and reports, including Power BI
• Use AI-assisted development tools while maintaining quality
• Collaborate with client engineering leadership and investment, operations, and technology stakeholders
• Spend regular time in the office and perform on-site client work