Governance Data Engineer

Careers at KKR•Boston, MA

6h•Onsite

About The Position

KKR is a leading global investment firm that offers alternative asset management as well as capital markets and insurance solutions. KKR aims to generate attractive investment returns by following a patient and disciplined investment approach, employing world-class people, and supporting growth in its portfolio companies and communities. KKR sponsors investment funds that invest in private equity, credit and real assets and has strategic partners that manage hedge funds. KKR’s insurance subsidiaries offer retirement, life and reinsurance products under the management of Global Atlantic Financial Group. The ADAPT (AI, Data, and Platform Technologies) Engineering team is integral to KKR's technological strategy, architecting and supporting the firm's foundational data and AI capabilities. This team is recognized as a key enabler for global scale and business transformation, driving excellence by evolving technology into robust, platform-based solutions that enhance agility and deliver material business impact. KKR is seeking a Lead Engineer to join the core ADAPT Engineering team. This is a pivotal, hands-on technical leadership role requiring deep technical expertise in modern data engineering and a proven ability to derive critical insights from complex, large-scale financial data. The successful candidate will be instrumental in designing and constructing world-class data engineering capabilities that efficiently process massive data pipelines, leverage state-of-the-art AI-powered insights and document extraction, and integrate seamlessly across diverse cloud-powered databases. This role requires defining the technical blueprint for how KKR structures, stores, and leverages data to power its AI and investment platforms, ensuring data integrity, performance, and accessibility for critical firm-wide services.

Requirements

Bachelor’s or master’s degree in computer science, Engineering, or a related field.
8+ years of experience in data engineering, data architecture, data management, or a related discipline, with meaningful experience leading enterprise-scale data initiatives in complex, highly controlled environments.
Strong communication and stakeholder management skills, with the ability to work effectively across engineering, product, analytics, and business teams, align on priorities, and drive execution across cross-functional partners.
Demonstrated expertise in data cataloging, metadata management, data lineage, and data governance, including implementation of frameworks that support control, auditability, and regulatory compliance.

Responsibilities

Lead the design and implementation of enterprise data management capabilities, with particular focus on a universal data catalog, metadata management, and standardized data definitions across critical platforms and domains.
Partner with business, technology, risk, and control stakeholders to define and operationalize semantic data models that improve consistency, usability, and interoperability of data for analytics, reporting, and AI-enabled use cases.
Drive the build-out and maturation of SOX-aligned data governance frameworks, including data ownership, lineage, controls, policy adherence, and documentation standards to support regulatory, audit, and operational requirements.
Simplify and modernize data entitlement models to enable scalable, policy-based access management that balances security, compliance, and ease of use across users, applications, and AI agents.
Establish operating models, standards, and governance processes that improve data discoverability, trust, and reuse while reducing fragmentation and manual effort across the data ecosystem.
Translate strategic data priorities into executable roadmaps, influence cross-functional delivery teams, and oversee implementation outcomes to ensure solutions are scalable, practical, and aligned to enterprise architecture and business objectives.
Identify opportunities to make the firm’s data foundation more AI-ready by improving metadata quality, access patterns, semantic structure, and governance controls required for trusted agentic AI adoption.
Serve as a senior contributor and thought partner within the data organization, helping shape best practices, manage stakeholder expectations, and drive measurable progress against data transformation goals.
Mentor and elevate the technical proficiency of engineers across multiple teams.