Human Capital Data Engineering Lead

TEAM OVERVIEW

KKR's Technology team is responsible for building and supporting the firm's technological foundation including a globally distributed infrastructure, information security, and the application and data platforms. The team drives a culture of technology excellence across the firm through efficient workflow automation, democratization of data through modern data and collaboration platforms, and more recently through research and development of Generative AI based tools and services. Technology is regarded as a key business enabler at KKR and is an important accelerator to drive towards global scale creation and business process transformation. The Technology team consists of highly technical and business-centric technologists with the ability to form strong partnerships across all of our businesses.

POSITION SUMMARY

KKR is seeking a Data Engineer to lead the design and construction of world-class data engineering capabilities for the Human Capital domain. This is a pivotal, hands-on technical leadership role requiring deep expertise in modern data engineering and a proven ability to derive critical insights from complex, sensitive people data. Partnering closely with Human Capital leadership and the Human Capital Engineering team, the successful candidate will define the technical blueprint for how KKR structures, stores, and leverages people data to power its analytics and AI platforms across the employee lifecycle — spanning talent acquisition, core HC, performance, compensation, learning, and HC operations. The role ensures data integrity, performance, accessibility, and — critically — the confidentiality and governance demanded by sensitive workforce, compensation, and performance data.

RESPONSIBILITIES

• Lead the Human Capital Data Engineering function and set the strategic direction for the design, modernization, and scaling of people data platforms, pipelines, and reporting capabilities across the employee lifecycle — from talent acquisition and onboarding through performance, compensation, learning and development, and HC operations.
• Own and drive the multi-year data engineering roadmap for Human Capital, establishing a consolidated people data platform and semantic models that enable consistent, trusted reporting and analytics across HC functions.
• Establish the target-state architecture for people data, including scalable data models, ingestion frameworks, data pipelines, and analytics-ready platforms that support workforce, talent, performance, compensation, and people-operations reporting needs.
• Partner with the Chief Human Capital Officer, HC Business Partners, Talent Acquisition, Total Rewards, Talent Management, Learning & Development, People Analytics, and HC Operations — as well as Human Capital Engineering — to align data engineering initiatives with the firm's people strategy and business priorities.
• Oversee the integration of complex internal and third-party data sources across the HC technology stack (e.g., core HCM, ATS, performance, compensation, LMS, engagement, and HR service delivery platforms), ensuring strong controls around data quality, reconciliation, lineage, and operational resilience.
• Lead the modernization of legacy HC data environments and reporting processes by introducing scalable, cloud-aligned, and highly governed engineering patterns that improve quality, transparency, timeliness, and reusability of people data.
• Build, lead, and develop a high-performing team of data engineers and technical leads, fostering strong execution, talent development, engineering discipline, and a culture of accountability and innovation.
• Establish and enforce best practices for data architecture, security, access models, and platform governance — with particular attention to the confidentiality of compensation, performance, and employee relations data and to global data privacy requirements (e.g., GDPR, CCPA).

QUALIFICATIONS

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline; advanced degree preferred.
• 12+ years of experience in data engineering, data architecture, or related technology roles, including significant experience leading teams and delivering large-scale enterprise data platforms.
• Deep experience designing and scaling data models, ETL/ELT pipelines, and analytics platforms that support complex business processes and cross-functional consumption; familiarity with the Human Capital / HC data domain and employee-lifecycle processes strongly preferred.
• Proven track record of leading enterprise data modernization programs, including data warehouse transformation, pipeline re-architecture, cloud migration, and operating model improvement.
• Expertise in modern data engineering and platform technologies, including SQL, Python, cloud-based data ecosystems, orchestration tools, and warehouse or lakehouse architectures.
• Hands-on familiarity with integrating best-of-breed HC / enterprise platforms (e.g., Workday, SAP SuccessFactors, or Oracle HCM; ATS, LMS, and HR service delivery systems) into governed enterprise data environments via APIs, event streams, and iPaaS integration patterns.
• Strong understanding of data governance, controls, lineage, security, and auditability requirements, including global employment and data privacy regulations (e.g., GDPR, CCPA) and their implications for people-data architecture.
• Excellent communication and executive presence, with the ability to translate complex technical concepts into business value and to influence senior stakeholders across Human Capital and Technology.

CRITICAL COMPETENCIES FOR SUCCESS

• Strategic mindset — able to design and communicate a long-term vision for the Human Capital data platform while delivering incremental business value each cycle.
• Stakeholder management — strong ability to engage, influence, and align diverse stakeholders across Human Capital, Finance, Legal, Compliance, and Technology functions globally.
• Execution discipline — skilled at prioritization, governance, and managing delivery across a multi-workstream portfolio without compromising quality.
• Data stewardship — deep commitment to confidentiality, controls, and responsible handling of sensitive people, compensation, and performance data.
• Team leadership — proven ability to build, develop, and retain high-performing engineering teams, set clear goals, and foster a culture of ownership and continuous learning.
• Analytical and problem-solving — strong skills in translating complex data challenges into scalable, governed engineering solutions.
• Change leadership — able to drive adoption of modern data practices and embed them into HC and technology workflows.
• Operating in ambiguity — demonstrated ability to lead large, high-visibility transformation efforts and deliver results across multiple concurrent priorities.