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

Description

Role

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.

Responsibilities

• 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

Education 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
• 5+ years in data engineering, data architecture, or a related technical role, with at least some experience in a private equity, investment management, or financial services setting; experience building data pipelines for AI/ML or GenAI applications is a strong differentiator

Desired Skills

Snowflake & Cloud Data Platform

• Strong proficiency in Snowflake core features (Tasks, Streams, Dynamic Tables) and experience using Snowpark (Python) for complex in-warehouse transformations
• Deep expertise in SQL and dbt (data build tool) for modular, version-controlled data modeling
• Demonstrated experience with system connectivity and API-based integrations, connecting third-party platforms (PMS, accounting software, CRMs, data feeds) to a central data warehouse via REST APIs, SFTP pipelines, or native connectors (e.g., Fivetran, dbt, or custom ETL); ability to troubleshoot and maintain data pipelines end-to-end

AI & GenAI Data Infrastructure

• Experience building data pipelines that support AI/ML or GenAI applications, including handling unstructured data (documents, PDFs, text) and preparing it for retrieval or model consumption
• Familiarity with vector databases and embedding infrastructure (Snowflake Cortex Search or native vector data types preferred given KSL's platform); general vector database experience (Pinecone, Weaviate, or similar) a plus
• Working understanding of retrieval-augmented generation (RAG) pipeline design and the data requirements of LLM-based applications
• Exposure to MCP (Model Context Protocol) or similar tool-calling/agentic integration patterns a plus

Modeling & Analytics Enablement

• Proven ability to organize data from multiple sources into a unified reporting layer, making it easy for business users to get the answers they need without navigating complex underlying systems
• Familiarity with accounting and finance fundamentals (e.g., financial statements, key performance metrics, capital structures) is a plus, since KSL's data consumers are primarily finance and investment teams, and an ability to speak their language accelerates collaboration
• Experience with BI and reporting tools (Sigma, Workiva, Power BI, or similar), with an eye for translating business questions into clear, actionable visualizations

Communication & Collaboration

• Exceptional verbal, written and listening communication skills
• Strong 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, including the ability to work with large datasets, frame and breakdown problems, and synthesize 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

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.