Lead Data Platform Engineer, People Analytics & AI

Moody

New York (NY)

On-site

USD 192,000 - 278,000

Full time

8 days ago

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Job summary

Moody's is seeking a senior data engineering leader to design and operate the enterprise People data platform powering workforce analytics, AI-enabled insights, and data-driven People solutions. You will lead the People Analytics lakehouse, ensure data quality, governance, and scalable data assets, partnering with HR, Privacy, Risk, Legal, and Technology teams across Moody's global operations.

This role emphasizes building reusable data patterns, lineage, and semantic layers to accelerate

Qualifications

8+ years of experience in data engineering, analytics engineering, platform engineering, or related disciplines.
Deep expertise in SQL, data modeling, and data architecture.
Experience building and operating modern cloud data platforms and lakehouse architectures.
Strong understanding of data pipeline orchestration, testing, monitoring, and reliability practices.
Experience implementing data governance, metadata management, lineage tracking, and data quality frameworks.
Experience with Workday data extraction and AI-ready architectures is preferred.
Familiarity with semantic layers, knowledge graphs, or AI-ready data architectures is a plus.
Experience with Microsoft Fabric, Azure Data Platform, Databricks, Power BI is preferred.
Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions.

Responsibilities

Design and operate the enterprise People data platform that powers workforce analytics, AI-enabled insights, and data-driven People solutions at Moody's.
Design and maintain the People Analytics lakehouse and data architecture, ensuring platform performance, entitlements, reliability, observability, and operational excellence.
Develop, monitor, and optimize data ingestion and transformation pipelines from HR, talent, learning, engagement, compensation, and related source systems.
Build and maintain canonical datasets — including the enterprise worker spine and governed workforce facts — serving as the authoritative sources of truth for workforce data across the People function.
Establish consistent data models, metric definitions, metadata, business rules, and semantic layers that can be reused across multiple analytics and AI use cases.
Implement data quality testing, lineage tracking, monitoring, and certification processes to ensure workforce data remains secure, compliant, and audit-ready.
Partner with People Data Product Owners to translate business requirements into scalable, well-documented data assets that support reporting, analytics, AI assistants, and future workforce applications.
Collaborate with Privacy, Risk, Legal, Security, and Technology teams to ensure appropriate governance standards, documentation, version control, and change tracking for all workforce data.
Create reusable patterns and foundational data layers that reduce fragmented reporting, eliminate duplicate data preparation, and accelerate solution development across the People function.