Principal Data Lead

Job Title: KKR Recruitment – Human Capital Data Engineering Lead

Company Name: Kohlberg Kravis Roberts & Co. (KKR)

Role: Human Capital Data Engineering Lead

Job ID / Requisition ID: 6211621004

Department: Technology / Enterprise Data Engineering

Employment Type: Full-Time

Job Location: New York, NY, United States

Work Mode: On-Site

Base Compensation Range: $200,000 – $230,000 USD / year (plus performance bonus and comprehensive benefits)

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.

Team Overview & Role Summary:

Technology is regarded as a key business enabler at KKR and an essential accelerator to drive global scale creation and business process transformation. As the Human Capital Data Engineering Lead, you will spearhead the design, architecture, and implementation of data pipelines, analytics infrastructure, and reporting solutions powering KKR’s global Human Capital and Talent organization. You will partner closely with HR leadership, Enterprise Data teams, and technical stakeholders to deliver high-integrity workforce analytics, data models, and automated integration feeds.

Key Responsibilities:

- Lead the end-to-end architecture, development, and maintenance of scalable data engineering pipelines and data models supporting global Human Capital functions.
- Ingest, transform, and harmonize data across diverse enterprise HR platforms (e.g., Workday, ATS/CRM systems, talent management, payroll, and benefits systems).
- Design and implement robust ETL/ELT pipelines and modern data warehouse architecture ensuring data quality, lineage, and governance.
- Partner directly with Human Capital executives, People Analytics leads, and business stakeholders to translate complex workforce questions into actionable reporting models and dashboards.
- Establish best practices for automated testing, continuous integration/continuous deployment (CI/CD), version control, and data pipeline monitoring.
- Implement strict data security, encryption, role-based access control (RBAC), and regulatory compliance for sensitive employee and compensation datasets.
- Mentor and provide technical direction to data engineers and analysts across data architecture, SQL optimization, and pipeline orchestration.

Eligibility Criteria & Qualifications:

Education: Bachelor’s or Master’s degree in Computer Science, Information Systems, Software Engineering, Data Engineering, or a related quantitative discipline.

Experience: 7–10+ years of progressive experience in data engineering, data warehousing, and business intelligence development.

Domain Expertise: Prior experience architecting or delivering data engineering solutions specifically for Human Capital, HRIS (such as Workday), or workforce analytics environments.

Required Skills & Technical Competencies:

Programming & Scripting: Expert proficiency in Python and advanced SQL (complex query optimization, stored procedures, window functions, and performance tuning).

Cloud Data Warehousing: Hands-on mastery of enterprise cloud data platforms (e.g., Snowflake, AWS Redshift, or Databricks).

ETL & Orchestration: Deep experience with data pipeline orchestration tools such as Apache Airflow, dbt, or equivalent modern data stack utilities.

API & System Integration: Strong familiarity with RESTful APIs, SOAP web services, and Workday Enterprise Interface Builder (EIB) / Workday Studio / RaaS reports.

Data Governance & Security: Thorough understanding of data privacy frameworks, PII handling standards, and sensitive data access models within financial services.

Communication & Leadership: Outstanding ability to articulate technical data concepts to senior non-technical business leaders and drive cross-functional alignment.

Preferred / Good to Have:

- Experience within financial services, investment management, private equity, or banking environments.
- Hands-on experience developing semantic layers and enterprise BI models in Tableau, Power BI, or Looker.
- Cloud infrastructure certifications (AWS Certified Data Engineer, Snowflake SnowPro, or Databricks Certified Data Engineer).