Quantitative Developer - Asset Based Finance

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. References to KKR’s investments may include the activities of its sponsored funds and insurance subsidiaries.

We are seeking a Software Engineer with strong quantitative skills to join KKR's Investment Technology team in Dublin. This individual will design and build scalable data and analytics systems that power investment decision-making, portfolio monitoring, and asset-backed investment decisions.

This role sits at the intersection of software engineering, quantitative analysis, and financial modelling. You will develop robust data platforms and analytical tools that enable complex modelling, structured credit analysis, forecasting, and performance analytics. The position requires deep technical expertise, strong problem-solving ability, and intellectual curiosity within a sophisticated investment environment.

The ideal candidate has a background in computer science, engineering, applied mathematics, physics, or a related quantitative field and thrives in building long-term, high-impact systems as part of a global team.

• Design and develop high-performance systems that support structured credit analytics, asset-backed modeling, and portfolio-level forecasting.
• Implement robust data pipelines and computational frameworks that enable large-scale quantitative analysis.
• Partner with investment professionals to translate complex modelling requirements into production-grade systems.

• Develop infrastructure to support scenario analysis, performance attribution, risk analytics, and valuation models.
• Build reusable quantitative libraries and analytical tools to enhance modeling consistency and transparency.
• Improve automation across reporting and analytical workflows to accelerate investment decision-making.

• Design and implement scalable data architecture integrating internal investment data and third-party sources.
• Ensure performance, reliability, and extensibility of systems supporting large, structured datasets.
• Optimize compute and storage strategies to support advanced modelling use cases.

• Reduce reliance on external vendors through development of internal quantitative and data capabilities.
• Create a scalable foundation for future innovation across forecasting, AI-driven analytics, and performance visualization.
• Contribute to engineering standards, best practices, and long-term architectural direction.

• Implement strong controls for data integrity, validation, and reproducibility of quantitative outputs.
• Ensure appropriate security, compliance, and governance standards across systems and workflows.

• 8+ years of experience in software engineering, quantitative development, data engineering, or applied data science.
• Strong programming skills in Python and SQL, with experience building production systems. Experience with distributed computing frameworks such as Spark is a plus.
• Experience designing data models and scalable system architectures in modern data environments such as Snowflake or similar platforms.
• Hands-on experience building data pipelines using tools such as dbt, Dagster, or equivalent orchestration frameworks.
• Strong quantitative background with experience in financial modeling, structured products, risk analytics, or performance attribution.
• Solid understanding of cloud architecture, Linux environments, and containerization technologies such as Docker and Kubernetes.
• Strong problem-solving skills with the ability to work across engineering and investment teams.
• High level of ownership, integrity, and a collaborative mindset within a performance-driven culture.
• Experience with visualization tools such as Tableau or Power BI, and familiarity with structured credit analytics tools such as Intex, is beneficial.

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