TEAM OVERVIEW

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.

POSITION SUMMARY

KKR is seeking a Software Engineer to join the core Data Platform Engineering team in Boston or NYC. This individual will serve as the technical anchor for the team responsible for Data Explorer, the firm’s primary governed data exchange platform, and for the data solutions that form the platform’s intelligence layer.

Data Explorer is evolving from an internal data catalog into a unified, governed interface for discovering and accessing structured data products, AI-powered document analysis, business intelligence assets, and secure external client data. In parallel, the team is building unified Data Rooms and AI-powered analytics experiences that provide users with governed answers and insights—not simply access to raw tables and reports.

This is a pivotal, hands-on technical leadership role with ownership of the end-to-end architecture across modern web applications, API and service layers, cloud data platforms, identity and access controls, AI-enabled user experiences, and business intelligence integrations. The successful candidate will set engineering standards, mentor senior engineers, and lead the team through its most complex cross-cutting design challenges, including external-facing security, AI and user-experience integration, and BI platform consolidation.

The role operates within a Data Engineering and Data Operations model in which Data Operations owns business requirements and roadmap priorities—the “what”—while the Principal Software Engineer leads technical design, implementation strategy, and engineering execution—the “how.”

This is an onsite role, with an expectation of working from KKR’s Boston office four days per week, or the New York City office five days per week. 

KEY RESPONSIBILITIES

• Own the technical architecture and guide roadmap execution for Data Explorer, including its React and TypeScript front end, solution backend, API and service layers, and cloud data integrations.

• Translate product and operational priorities into scalable technical designs across Data Explorer’s core capability areas: report consolidation, persona-based landing experiences, Sigma integration, AI and Data Rooms, external client access, and administrative and user-support capabilities.

• Lead the design and delivery of secure external client access, including dedicated authentication flows, client-specific entitlements through Saviynt, and simplified user experiences for non-KKR users.

• Establish secure, scalable access patterns for external clients and constituents, including ABF, TPA, and LP users, within a SOX-aligned control environment.

• Build the AI-powered data solutions layer, including unified Data Rooms that replace legacy data collections and provide governed access to structured data models and indexed document repositories.

• Deliver embedded, chat-based natural-language experiences that allow users to query structured and unstructured data, analyze documents, and receive contextual answers and insights.

• Integrate Data Explorer with KARA 3.0 and the firm’s agent marketplace, enabling Data Operations teams to deploy governed, business-facing agents through the platform rather than through bespoke application builds.

• Drive BI consolidation and integration by establishing native Sigma and Power BI experiences within the Data Explorer inventory, supporting the retirement of the legacy report engine, and evaluating Sigma-based interactive exploration as an alternative to the existing AG Grid Data Access experience.

• Data distribution as part of the Unified Data Fabric team to consume governed Gold-layer data products and ensure that platform experiences align with enterprise data product, metadata, lineage, quality, and interoperability standards.

• Integrate Data Explorer with the firm’s persona and access-package entitlement model, working closely with ADAPT Engineering teams responsible for data fabric, MKS, Saviynt, and related platform capabilities.

• Define reusable architecture and engineering patterns for APIs, authentication, authorization, data access, AI integration, observability, testing, deployment, and operational support.

• Ensure that the platform meets enterprise expectations for security, reliability, performance, scalability, auditability, data classification, and regulatory controls.

• Set the engineering bar through architecture reviews, technical standards, design documentation, code quality practices, and mentorship of senior engineers and technical leads.

• Lead complex cross-team design decisions and resolve architectural issues that span application engineering, data engineering, AI, identity, BI, infrastructure, and operational workflows.

• Partner with engineering, product, architecture, analytics, Data Operations, and business leaders to sequence investments, manage technical dependencies, and align delivery with the firm’s broader data and analytics strategy.

• Evaluate emerging technologies and market developments in AI-powered analytics, agent platforms, data discovery, BI integration, identity, and enterprise data platforms, translating relevant developments into practical architectural recommendations.

QUALIFICATIONS

Required

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field; advanced degree preferred.

• Ten or more years of software engineering experience, with a demonstrated track record of owning architecture for user-facing data platforms, enterprise web applications, or similarly complex products at scale.

• Expert full-stack engineering skills, including modern React and TypeScript front-end development and API or service-layer development over cloud-based data platforms.

• Strong experience designing and operating platforms using technologies such as Snowflake, lakehouse or Apache Iceberg architectures, and AWS services.

• Deep experience with enterprise identity and access management, including SSO, RBAC and ABAC entitlement models, identity governance integrations such as Saviynt, and secure external or client-facing access patterns.

• Hands-on experience delivering AI-powered product capabilities, such as LLM integration, retrieval over structured and unstructured data, natural-language query interfaces, AI-assisted document analysis, or enterprise agent platforms.

• Experience embedding or integrating business intelligence tools such as Sigma, Power BI, or Tableau into a unified product experience.

• Strong proficiency in SQL and Python, with the ability to work effectively across application, data, integration, and AI technology layers.

• Demonstrated ability to design secure, scalable, reliable, and observable systems that support both internal users and external clients.

• Proven technical leadership, including driving cross-team architecture decisions, establishing engineering standards, mentoring experienced engineers, and influencing outcomes without relying on direct authority.

• Ability to operate at both strategic and hands-on technical levels, translating ambiguous requirements into durable platform capabilities and actionable engineering plans.

• Strong communication and stakeholder-management skills, with the ability to explain complex technical decisions to engineering, product, operations, architecture, and business audiences.

Preferred

• Experience in financial services or another highly regulated industry, including familiarity with SOX-aligned controls, auditability, data classification, and governed access to sensitive information.

• Experience with event-driven architectures and technologies such as Kafka.

• Familiarity with enterprise data products, data catalogs, data marketplaces, semantic layers, metadata management, lineage, and governed self-service data access.

• Experience modernizing or consolidating enterprise BI and reporting ecosystems.

• Experience partnering with product or operations owners in a split operating model in which those teams own requirements and roadmap priorities while engineering owns technical design and execution.

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