Data Lead

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.

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.

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.