Data Architect

KSL CAPITAL PARTNERS – Data Architect – Denver, CO

Description:

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.

Role:

To support KSL’s continued growth and evolving data strategy, the Data & AI team is seeking a Data Architect, to own the firm’s data infrastructure and enable seamless, reliable data flow across the organization. Reporting to the Chief Technology Officer, this role will be instrumental in unifying disparate data sources into a cohesive, scalable architecture and will serve as a critical technical partner across FP&A, Deal Teams, Strategic Operations, Fund Operations, and Portfolio Companies. This role serves as the primary technical counterparty to external consultants, responsible for reviewing architectural decisions, ensuring long-term maintainability, and leading the gradual transition of data warehouse ownership in-house.

Working in close coordination with a dedicated team of external consultants, this position will direct and participate in the design, construction, and maintenance of the data pipelines, integrations, and infrastructure that power KSL’s reporting and analytics capabilities. The ideal candidate is a hands-on technical contributor who brings strong data engineering fundamentals and a pragmatic, solutions-oriented mindset. This role sits within a growing Data and AI team and will serve as a foundational hire as the team scales. The ideal candidate knows when to leverage consultants or external resources to accelerate delivery and embracing AI tools to work efficiently and drive higher output. They must be comfortable interfacing directly with internal leadership and portfolio company teams to translate business needs into reliable data solutions.

Key Responsibilities:
•Own and evolve the firm’s Snowflake data platform, ensuring it is secure, performant, cost-effective, and structured to support dynamic reporting and self-serve analytics across the firm
•Design and operate reliable data ingestion and transformation workflows that unify data from portfoliocompanies, from property management systems and ERPs to CRMs and custom data feeds into a unified,reliable enterprise data layer in Snowflake
•Develop and maintain the Snowflake data platform, implementing role-based security, performance tuning, andcost-optimization practices to ensure a production-grade environment
•Partner closely with external consultants, finance, deal teams, and operations to deliver trusted, self-servereporting and analytics providing clean, well-modeled datasets that powers BI tools like Sigma, Power BI, orTableau
•Assist with the end-to-end data onboarding process for new investments partnering with internal teams andportfolio company operators to map source systems, data extraction and ingestion pipelines into Snowflake,define key metric taxonomies, and validate data quality from day one
•Establish and document data architecture, definitions, and governance standards to ensure data consistencyand quality firm-wide
•Actively leverage AI tools and automation including AI-assisted coding, data pipeline generation, and anomalydetection to work efficiently, reduce manual effort, and elevate the quality and speed of data delivery acrossthe firm
•Establish and enforce standardized data models, naming conventions, and metric definitions across portfolio companies to ensure consistency in how performance data is captured, stored, and surfaced for monitoring, valuations, and investor reporting

Desired Skills and Experience:
•Bachelor's Degree in Computer Science, Information Systems, Data Engineering, or a related technical field; degrees in Finance, Economics, or Accounting combined with strong technical experience will also be considered
•8+ years of progressive experience in data engineering and architecture, including a minimum of 3 years designing end-to-end Snowflake environments and semantic layers. Prior experience in private equity, investment management, or financial services is desired
•Master Data Management (MDM) experience to support the delivery of a clean, reliable data environment for business consumers

Snowflake & Cloud Data Platform Expertise
•Hands-on experience building and operating data pipelines using automation tooling such as Python, Fivetran, Matillion, dbt, Cloud Functions, Snowflake Tasks, and Stored Procedures

•Advanced SQL expertise, with experience building and maintaining complex queries, views, and transformations, as well as Snowflake Tasks and Stored Procedures (SQL and Python) to implement and orchestrate core data logic
Data Engineering & Pipeline Development
•Experience building and operating scalable ETL/ELT pipelines using tools such as Fivetran, dbt, or equivalent frameworks

•Demonstrated ability to integrate third-party systems (PMS, accounting platforms, CRMs, data feeds) via REST APIs, SFTP pipelines, native connectors, or custom ingestion logic, and to troubleshoot pipelines end-to-end

Architecture, Modeling & Analytics Enablement
•Ability to integrate disparate data sets from SaaS platforms (Juniper Square, Maybern, Workiva) into a single Snowflake "master record," with robust cross-platform reconciliation and lineage tracking for how the master record is formed

•Experience designing and maintaining semantic models that define standardized metrics, dimensions, and business logic used consistently across BI tools and reporting surfaces

•Familiarity with BI and analytics tools (Sigma, Power BI, Tableau, or similar) and translating business questions into clear, reliable datasets and visualizations

•Comfort partnering with finance and investment teams, with working knowledge of financial statements, performance metrics, and capital structures

AI-Assisted Development & Modern Practices
•Proficiency with AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude, ChatGPT) and a demonstrated habit of leveraging AI to accelerate data engineering work including pipeline development, SQL generation, documentation, and data quality checks

Communication & Collaboration
•Strong communication and project management skills, with a demonstrated ability to work effectively across functions, manage competing priorities independently, and translate technical concepts for non-technical stakeholders
•Ability to operate both strategically and tactically — comfortable diving into the details to solve problems while also planning for long-term growth
•Excellent operational, organizational and follow-up skills with the ability to manage and process complex operational work
•Strong problem-solving and data analytical skills – the ability to work with large datasets, frame and break down problems, synthesis themes and insights from analyses. Problem solving includes both quantitative and qualitative information and problems
•Ability to multitask and prioritize without feeling overwhelmed and quickly pivot from one task to another
•Familiarity with accounting and finance fundamentals (e.g., financial statements, key performance metrics,capital structures) is a plus — KSL's data consumers are primarily finance and investment teams, and anability to speak their language accelerates collaboration
•Exceptional verbal, written and listening communication skills

The above statements are intended to describe the general nature and level of work performed by employees assigned to this classification. The statements are not intended to be an exhaustive list of all job duties performed by employees assigned to this classification.