AI Data Engineer | Information Technology

KSL Capital Partners – Information Technology - AI Data Engineer – Denver, CO

KSL Capital Partners, LLC ("KSL") is a leading global private equity firm specializing in travel and leisure enterprises. KSL specializes in investments across five primary sectors: hospitality, recreation, clubs, real estate, and travel services. KSL has approximately $25 billion of assets under management across its equity, debt, and tactical opportunities funds and has completed over 185 investments since 2005. These investments include some of the premier businesses and properties in travel and leisure globally.

Today, KSL has offices in Denver, Colorado;
Stamford, Connecticut;
New York City, New York; and London, England.

To support KSL’s continued growth and evolving data and AI strategy, the Data & AI team is seeking an AI Data Engineer to build, scale, and maintain the firm’s data infrastructure, with a specific mandate to prepare, structure, and pipeline data for AI and GenAI-driven data products. Reporting directly to the Data Architect, this role is responsible for the technical execution of KSL’s data roadmap.

This new hire will be the primary "builder" responsible for developing the pipelines and integrations that unify disparate data sources, both structured and unstructured, into a cohesive Snowflake environment, enabling reliable data flow and AI-native data products for FP&A, Deal Teams, and Portfolio Companies.

Working in close coordination with the Data Architect and a dedicated team of external consultants, this position is a hands-on technical role focused on the construction and operational excellence of our data ecosystem, spanning both traditional structured data pipelines and the infrastructure required to power AI and GenAI applications. The ideal candidate is a highly productive engineer who brings a "reliability-first" mindset to data modeling and pipeline development, along with genuine experience preparing data for AI/ML consumption.

As a foundational hire on a scaling team, you will embrace modern AI-assisted development tools to work efficiently and support the transition of data warehouse ownership in-house.

• Build and monitor robust ETL/ELT pipelines that ingest and transform data from portfolio companies, property management systems, ERPs, and SaaS platforms (Juniper Square, Maybern, Workiva) into Snowflake
• Execute data modeling tasks in Snowflake to create clean, well-modeled, and performant datasets that power self-serve analytics and dashboards in Sigma and Workiva, as well as direct consumption by Claude, ChatGPT, and AI agents for querying, reporting, and automated workflows
• Design and build ingestion pipelines for unstructured and semi-structured data (offering memoranda, DDQs, LP agreements, and other deal and portfolio documents stored in Box), parsing and structuring content to support retrieval-augmented generation (RAG) and AI-assisted analysis
• Build and maintain embedding and vector infrastructure, primarily leveraging Snowflake Cortex Search and native vector data types, to enable governed, high-quality retrieval for Claude and other AI applications querying KSL’s data
• Partner with the Data Architect to extend Snowflake’s data model to support AI-specific consumption patterns, including metadata tagging, lineage tracking, and access controls appropriate for AI-driven queries and agents
• Support the technical onboarding of new investments, assisting with source-to-target mapping, API integrations, and validation of data quality from day one
• Implement data quality checks and anomaly detection within the pipeline to ensure the "Golden Record" remains the trusted source of truth for both traditional reporting and AI-driven data products
• Collaborate daily with the Data Architect to translate architectural blueprints into functional, maintainable code and automated workflows, including the integration of AI/agentic tooling (e.g., MCP connections) with Snowflake and other core platforms

• Bachelor’s Degree in Computer Science, Information Systems, Data Engineering, or a related technical field; degrees in Finance, Economics, or Accounting…