Reference Data Engineer

About the role

COMPANY OVERVIEW

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.

TEAM OVERVIEW

KKR's Technology organization is a group of passionate technologists and product managers, unified by a shared mission to deliver exceptional products and solutions that drive value for our stakeholders, clients, and investors. Our passion for technology and innovation fuels our commitment to creating high-quality, impactful solutions that address complex challenges and meet the evolving needs of our sophisticated businesses.

POSITION SUMMARY

We are seeking an Assistant Vice President (AVP), Software Engineering to join our Reference Data Technology team. This role blends senior individual contribution with emerging technical leadership: you will design and deliver robust backend services and data pipelines while helping guide the technical direction of your team. The ideal candidate is a strong Python developer with hands-on experience in distributed data processing (Spark), infrastructure-as-code (Terraform), and deep data engineering fundamentals spanning data modeling, ETL, and data distribution. You will own significant workstreams end-to-end, raise the engineering bar through code reviews and design input, and mentor junior engineers as you continue to grow toward a lead or vice president level.

KEY RESPONSIBILITIES

- Design, build, and own scalable backend services and data pipelines primarily in Python.

- Develop and optimize large-scale data processing workflows using Apache Spark for batch and near-real-time use cases.

- Lead data modeling and schema design decisions that support reliable analytics and downstream consumption.

- Architect and maintain robust ETL/ELT workflows with strong data quality, lineage, and observability.

- Design and implement data distribution patterns to serve data efficiently across internal systems and consumers.

- Provision and manage cloud infrastructure using Terraform and infrastructure-as-code best practices.

- Own delivery of significant workstreams end-to-end, from technical design through production and operations.

- Drive engineering quality through design reviews, code reviews, and clear technical standards.

- Mentor and support the growth of analysts and junior engineers on the team.