Principal Data Lead (Data Engineering with Full-Stack Dev) - Investment Systems

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.

•    Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
•    12+ years of professional software engineering experience, including significant time as a technical lead or architect of data-intensive platforms.
•    Expert-level proficiency in Python for building production services, data pipelines, and platform frameworks.
•    Proven experience architecting master data management (MDM) or reference/entity-data systems — entity resolution, survivorship, golden records, crosswalk, and lineage.
•    Deep data engineering expertise: data modeling, ETL/ELT design, distributed processing (Spark), and data distribution patterns at scale.
•    Hands-on experience designing API-first services and event-driven / streaming architectures (e.g., Kafka, CDC).
•    Strong command of modern data storage — relational, non-relational, and lakehouse formats (e.g., Apache Iceberg) — and SQL.
•    Experience with infrastructure-as-code (Terraform) and cloud-native deployment (containers, Kubernetes/EKS, CI/CD).
•    Deep domain experience with reference and master data in the financial industry — parties / legal entities, instruments, portfolios, and accounts — and the identifier, hierarchy, and cross-referencing challenges specific to it.
•    Hands-on experience integrating and mastering market data and vendor/administrator feeds (e.g., Bloomberg, exchange, and fund-servicing sources), including normalization, cross-referencing, and point-in-time handling.
•    Practical experience applying Generative AI / LLMs to data platforms — e.g., grounded retrieval over governed data, entity-resolution and data-quality assistance, or AI/agent-ready data access with provenance guarantees.
•    A track record of setting technical direction, driving architecture across teams, and mentoring senior engineers.
•    Excellent judgment on architectural trade-offs, and the communication skills to align engineering, product, and business stakeholders.

PREFERRED QUALIFICATIONS
•    Familiarity with bitemporal / point-in-time data modeling and governed, auditable data systems.
•    Experience with workflow orchestration (e.g., Dagster, Airflow) and lakehouse catalogs (e.g., Polaris, Nessie, Glue).
•    Exposure to commercial or vendor MDM/ontology platforms (e.g., Palantir Foundry) and experience integrating them as pluggable engines within an open architecture.
•    Experience building agentic workflows or retrieval-augmented systems on top of enterprise data, and familiarity with LLM evaluation, guardrails, and grounding techniques.
•    Experience with legal-entity identifiers and standards (e.g., LEI, GLEIF) and regulatory reference-data requirements.