Reinsurance Data Engineer

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. Within ADAPT, the Insurance & Reinsurance Data Engineering function is responsible for designing, governing and enabling the enterprise data architecture that powers KKR's reinsurance accounting, treaty management, risk analytics and regulatory reporting capabilities.

POSITION SUMMARY

KKR is seeking a Reinsurance Accounting & Risk Data Engineer to join the ADAPT Engineering team in Boston. This is a pivotal, hands-on technical leadership role requiring deep expertise in reinsurance accounting data, risk data architecture, data modeling and modern Data Fabric design. The successful candidate will lead the design and strategic evolution of the enterprise data architecture supporting KKR's reinsurance accounting, treaty management and risk analytics functions — ensuring seamless integration, reconciliation and accessibility across ceded and assumed reinsurance portfolios, counterparty risk systems and financial reporting platforms.

Partnering closely with reinsurance operations, actuarial, finance, risk management and technology teams, this individual will ensure that data related to treaty structures, ceded/assumed premiums, loss recoveries, reserves, collateral, counterparty exposures, regulatory capital and risk aggregation is accurate, consistent, well-modeled and effectively leveraged for accounting close processes, regulatory compliance, risk analytics and strategic decision-making.

This role requires defining the technical blueprint for how KKR structures, stores and leverages reinsurance and risk data to power its insurance platforms, ensuring data integrity, performance and accessibility for critical firm-wide services.

This is an onsite role, with expectations to be in our Boston offices 4 days per week.

KEY RESPONSIBILITIES

· Design and implement a scalable Data Fabric architecture enabling unified, real-time access to reinsurance accounting and risk data across distributed systems and business domains.

· Define and maintain enterprise data architecture vision, principles, standards and reference architectures for the reinsurance data ecosystem aligned with KKR's ADAPT strategy.

· Lead development of enterprise-grade conceptual, logical and physical data models for treaty structures, ceded/assumed accounting, loss recoveries, collateral, counterparty risk and regulatory capital.

· Establish and enforce data modeling standards, naming conventions and best practices ensuring consistency, reusability and scalability across all reinsurance and risk data assets.

· Implement robust data governance frameworks, data quality standards and stewardship processes ensuring compliance with STAT, GAAP, IFRS 17, LDTI, Solvency II, BSCR and RBC requirements.

· Establish data lineage, cataloging, metadata management and reconciliation frameworks as foundational components of the reinsurance Data Fabric architecture.

· Partner with Reinsurance Operations, Treaty Accounting, Actuarial, Finance and Risk teams to understand data needs and strategic use cases across the reinsurance lifecycle.

· Enable risk aggregation, catastrophe modeling, counterparty exposure analytics and ensure data readiness for advanced analytics and AI/ML workloads.

· Act as a reinsurance domain expert advising on treaty terms, ceded/assumed accounting, reserve methodologies, counterparty credit risk, retrocession and regulatory capital impact.

· Provide technical leadership across the modern data stack including Snowflake, AWS (S3, Glue, Redshift), Databricks and Azure Synapse for reinsurance data solutions.

· Mentor and guide junior architects, data engineers and analysts on reinsurance data architecture best practices, data modeling standards and domain knowledge.

QUALIFICATIONS

· Bachelor's or master's degree in Computer Science, Information Systems, Data Science, Actuarial Science, Finance, Mathematics, or a related field.

· 10+ years of relevant professional experience in enterprise data architecture, data modeling, data management or data engineering with a strong focus on reinsurance accounting and/or insurance risk management within the insurance or financial services industry.

· Proven experience designing and implementing Data Fabric or similar enterprise data integration architectures (Data Mesh, data virtualization, metadata-driven architectures) in a reinsurance or insurance context.

· Strong understanding of the reinsurance domain including treaty types (proportional, non-proportional, facultative, retrocession), ceded/assumed accounting, settlement processes, reinsurance receivables/payables, collateral management, loss recovery and accounting and counterparty credit risk.

· Deep knowledge of reinsurance accounting standards and regulatory frameworks including STAT, GAAP, LDTI, Solvency II, BSCR, RBC.

· Hands-on technical expertise with SQL and data querying/analysis to explore, validate, reconcile and troubleshoot reinsurance data issues independently. Proficiency in Python or equivalent programming language preferred.

· Experience with modern cloud data platforms (e.g., Snowflake, AWS Redshift, Databricks, Azure Synapse) and data integration/ETL frameworks.

· A strategic technical mindset focused on automation, continuous improvement and delivering measurable business outcomes in a dynamic, fast-paced environment with complex multi-entity reinsurance structures.

· Excellent communication, stakeholder management and the ability to translate complex reinsurance and risk data concepts for diverse audiences including senior leadership.