Senior Data Platform Engineer — People Analytics & AI

PowerToFly; New York (NY); On-site; USD 192,000 - 278,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

401(k) matching

Paid time off

tuition reimbursement

Job summary

Engineers will own data ingestion, canonical datasets, and semantic layers, and work with privacy and risk teams to maintain compliance. The role emphasizes reliability, observability, and audit-ready data while accelerating solution development across the organization.

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.
• 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

SQL

Data modeling

Data architecture

Cloud data platforms

Data governance

Workday data extraction

AI/ML

Power BI

Azure Databricks

Education

Bachelor's degree in Computer Science or related field

Tools

Microsoft Fabric

Azure Data Platform

Databricks

Power BI semantic models

Job description

Engineers will own data ingestion, canonical datasets, and semantic layers, and work with privacy and risk teams to maintain compliance. The role emphasizes reliability, observability, and audit-ready data while accelerating solution development across the organization.