Job Description

TEAM OVERVIEW

KKR’s Global Data Operations team is responsible for collecting, managing, governing, and harnessing data across the firm. Within Data Operations, the Data Operations Center of Excellence (CoE) operates as a hub-and-spoke data delivery engine, pairing functional spokes with deep business expertise and a central delivery hub with scalable data, analytics, governance, reporting, and core data management capabilities.

The Distribution Data Enablement team is the functional spoke supporting KKR’s Distribution organization, delivering trusted data and analytics solutions across client and LP data management, operational controls and reconciliations, fund performance, LP outcomes, relationship economics, fundraising trends, and broader business performance across asset classes.

We are seeking a Principal-level Distribution Data Enablement Lead to serve as the primary data and analytics partner for Distribution business performance, fund performance analytics, LP-level insights, and cross-asset-class commercial intelligence.

This role will help define and build a quantitative, forward-looking, and solution-oriented data enablement capability for Distribution. The individual will be responsible for developing scalable analytics, models, tools/automation, and data solutions that support decision-making across fundraising, product strategy, investor engagement, fund performance, LP outcomes, relationship economics, and broader commercial performance.

The successful candidate will design, build and lead advanced analytical capabilities, including the Python-based models, applications, automation, and self-service tools required to operationalize the function. These capabilities will help Distribution answer complex questions such as how fund performance changes if specific deals are included or excluded, how outcomes compare across strategies, how co-investment participation affects investor outcomes, how LP relationships perform economically over time, where profitability is concentrated across the client base, and how fundraising, product, and investor trends are evolving across the platform.

This is a highly hands-on technical leadership role. The individual must be able to partner with senior Distribution, Product Strategy, Finance, Investment, and Data/Technology stakeholders while personally building models, prototyping analytical tools, automating workflows, and modernizing the firm’s Distribution data and analytics environment. The role will require strong technical execution, commercial judgment, and the ability to influence strategic architecture decisions as the capability is built out.

RESPONSIBILITIES

• Serve as the primary analytics partner for Distribution business performance, fund performance analytics, LP-level insights, and investor economics
• Lead the evolution of the historical Performance Analytics function from a private markets-focused reporting capability into a broader cross-asset-class Distribution advanced analytics and data enablement function
• Build analytical models, Python-based data applications, automation, and self-service tools that enable scalable insight generation rather than one-off analysis
• Deliver high-quality ad-hoc analysis, performance insights, hypothetical scenarios, investor analytics, and business performance reporting for senior Distribution stakeholders
• Build reusable models that evaluate fund performance under different hypothetical scenarios, including inclusion or exclusion of specific deals, co-investment participation, investment timing, cash flow assumptions, and portfolio composition changes
• Develop LP-level analytics across asset classes, including investor performance, exposure, allocation, capital activity, product participation, and relationship-level economics
• Build profitability and commercial performance analytics for LP relationships, including revenue, economics, resource intensity, product participation, and long-term relationship value
• Support macro-Distribution business performance analysis, including fundraising trends, platform economics, investor segmentation, product performance, and concentration across LPs, channels, regions, and strategies
• Integrate Distribution, fund, investor, deal, finance, and accounting data to create richer analytical views of investor outcomes and business performance
• Modernize manual and Excel-heavy performance analytics processes through reusable models, governed datasets, standardized calculations, and automated workflows
• Partner with Technology and Data Operations to productionize prototypes, improve data pipelines, and strengthen the broader Distribution analytics infrastructure
• Translate complex fund, deal, LP, and commercial data into clear insights, executive-ready narratives, and decision-support materials
• Establish quality controls, documentation, and governance standards for recurring analytics, scenario models, dashboards, and decision-support tools
• Help define the long-term operating model for Distribution data enablement, including service model, toolset, prioritization framework, data standards, and partnership model with Product Strategy, Finance, Technology, and Distribution stakeholders

QUALIFICATIONS

• 10+ years of experience in investment analytics, performance analytics, investor analytics, quantitative analytics, financial analysis/ engineering, business performance analytics, or related roles within asset management, alternatives, investment banking, or financial services
• Deep understanding of private markets performance concepts/metrics, including fund structures, LP reporting, capital activity, valuations, fund cash flows, IRR, MOIC, TVPI, DPI, PME, benchmarking, performance attribution models, and deal-level contribution analysis
• Strong cross-asset-class orientation, with the ability to extend fund, investor, and business performance analytics across private equity, credit, real estate, infrastructure, insurance-related strategies, and other investment products
• Experience developing hypothetical and scenario-based analysis, including deal inclusion/exclusion, co-investment impact, cash flow projections, portfolio construction changes, and LP-specific performance scenarios
• Strong understanding of LP-level analytics, including investor exposure, product participation, capital commitments, unfunded balances, subscriptions, redemptions, distributions, realized/unrealized performance, and relationship economics
• Experience with business performance or profitability analytics, including client profitability, revenue attribution, relationship value, platform economics, segmentation, concentration, and commercial trend analysis
• Familiarity with financial books and records, fund accounting, investment accounting, and performance data environments; experience with platforms such as Investran, Geneva, Salesforce, or similar systems preferred
• Highly hands-on technical coding skills, including advanced Python capability and experience building analytical tools, applications, automation, reusable models, and scalable reporting solutions
• Advanced SQL and database manipulation skills, with the ability to work directly with large, complex datasets across fund, deal, investor, performance, fundraising, revenue, and commercial domains ( Snowflake or Databricks experience strongly preferred)
• Experience with Sigma, Power BI, Tableau, or similar business intelligence platforms, including dashboard design, semantic data modeling, governed metrics, and self-service analytics
• Demonstrated ability to reduce reliance on manual Excel-based workflows and replace them with scalable, controlled, automated, and transparent analytical processes
• Strong commercial judgment, with the ability to connect analytical outputs to Distribution strategy, investor engagement, product decisions, and business performance outcomes
• Ability to operate effectively with senior stakeholders while managing fast-moving ad hoc requests and longer-term capability-building priorities
• Strong communication skills, with the ability to explain complex quantitative, data, performance, and profitability concepts clearly to technical and non-technical audiences
• Advanced degrees in finance, economics, mathematics, statistics, engineering, computer science, operations research, or another quantitative discipline preferred
• CFA, CAIA, FRM, or similar professional designation helpful but not required

PREFERRED PROFILE
The ideal candidate is a commercially oriented quant who can help Distribution understand fund performance, LP outcomes, relationship economics, and business performance at a deeper level than traditional reporting. This person should be able to frame the analytical question, source, structure, and validate the data, build the model or tool, pressure-test the results, and translate the output into clear business insights.

This is a hands-on builder and analytical leader, not simply a report producer. The right candidate will be comfortable moving between deal-level economics, fund performance, LP profitability, data architecture, Python-based modeling, dashboard/ visualization design, and senior stakeholder engagement.

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