Senior Data Platform Engineer: AI-Powered People Analytics

Moody's Corporation; New York (NY); On-site; USD 192,000 - 278,000; Full time

Job summary

Moody's Corporation is seeking a senior leader to design and operate the enterprise People data platform, powering workforce analytics, AI-enabled insights, and data-driven People solutions. You will own the People Analytics lakehouse, data architecture, governance, and reliability across Moody's.

You will develop, monitor, and optimize ingestion pipelines from HR, talent, learning, engagement, and compensation systems; build canonical datasets and semantic layers; and partner with

Qualifications

• 8+ years of experience in data engineering, analytics engineering, platform engineering, or related disciplines.
• Deep expertise in SQL, data modeling, and data architecture with proven ability to design scalable, reusable data structures.
• 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.
• Ability to translate business concepts and workforce requirements into scalable technical data structures and governed data assets.
• Experience with Workday data extraction (RaaS, WQL, Core Connectors) and effective-dated HCM data models 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 semantic models, or similar technologies is preferred.
• Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation and operational efficiency.

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.

Skills

Data engineering

SQL & data modeling

Cloud data platforms

Data pipelines & orchestration

Data governance & quality

Workday data extraction

Azure Fabric / Databricks

AI in analytics

Education

Bachelor's degree

Tools

Databricks

Power BI

Microsoft Fabric

Azure Data Platform

Job description

Moody's Corporation is seeking a senior leader to design and operate the enterprise People data platform, powering workforce analytics, AI-enabled insights, and data-driven People solutions. You will own the People Analytics lakehouse, data architecture, governance, and reliability across Moody's.

You will develop, monitor, and optimize ingestion pipelines from HR, talent, learning, engagement, and compensation systems; build canonical datasets and semantic layers; and partner with