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. POSITION SUMMARY We are seeking a Principal Data Lead to set the technical direction of our Reference Data Platform — the system of record that masters, governs, and distributes the firm’s core reference data (parties, instruments, portfolios, and accounts. This is a hands-on technical leadership role: you will own the end-to-end architecture of a layered, event-driven data platform — spanning sourcing, ingestion, entity mastering, a bitemporal golden copy, and an API-first distribution layer — and drive it from vision to production. You will be the senior-most engineering voice on reference data, shaping platform strategy, establishing engineering standards across teams, and partnering with product, data, and infrastructure leaders to deliver a scalable, vendor-agnostic master data capability. The ideal candidate is an expert Python engineer and data architect with deep experience in master data management (MDM) and reference data within the financial industry — including market data and vendor feeds — combined with hands-on experience applying Generative AI to data platforms, distributed data processing, and building reusable, independently scalable platform components. We are operating in a 4-day in office, 1-day flexible work arrangement.

QUALIFICATIONS • 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.

We are operating in a 4-day in office, 1-day flexible work arrangement. ROLES & RESPONSIBILITIES • Own the architecture of a layered reference-data platform, defining the contracts between sourcing, ingestion, mastering, golden-copy, and distribution layers so each is independently buildable, deployable, and scalable. • Set technical direction for entity mastering — standardization, blocking, matching, survivorship, and crosswalk/lineage — designing rules as configuration (data, not code) that scale from exact-match fast paths to full-population fuzzy resolution. • Architect the durable golden copy: a bitemporal, point-in-time source of truth with match evidence, per-attribute provenance, and a schema-last serving model that adapts to new sources and domains without migration. • Define the event-driven backbone and cadence model (start-of-day, intraday, end-of-day, and on-demand) enabling batch today and real-time streaming (CDC / Kafka) without re-architecture. • Design the API-first, self-documenting distribution surface (REST, curated data products, subscriptions) governed by data contracts and consumable by applications, analytics, and AI/agents. • Drive the platform’s storage and compute strategy over an open lakehouse (Apache Iceberg, Snowflake) and elastic compute (Spark), ensuring an open, vendor-agnostic, no-lock-in foundation. • Establish engineering standards, reference patterns, and a reusable component library (a layered monorepo) that make capabilities build-once, reuse-everywhere across domains. • Lead cross-team technical design reviews, resolve the hardest architectural trade-offs, and align engineering execution with the firm’s data strategy. • Champion data quality, governance, security, entitlements, audit, and observability as first-class, cross-cutting foundations of the platform. • Mentor senior and staff engineers, grow technical depth across the team, and set the bar for engineering excellence. • Partner with product and business stakeholders to translate reference-data needs into a durable, scalable platform roadmap. QUALIFICATIONS

• 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. KKR is an equal opportunity employer. Individuals seeking employment are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, sexual orientation, or any other category protected by applicable law.