HR Data Engineer

Employment Type: Full-time

Position Overview

The People Analytics Center of Excellence (CoE) is seeking an HR Data Engineer to support the development, enhancement, and management of data engineering, data automation, and data analytics solutions. This role is responsible for enhancing our existing HR data foundation (Databricks) by designing, building, and maintaining scalable data pipelines and integrations. These pipelines will enable accurate, secure, and timely access to HR data across the organization. The HR Data Engineer will build data models, optimize databases to improve decision-making, and evaluate the integrity, quality, and reliability of data outputs and outcomes. This role involves close collaboration with the broader People Analytics team, IT, and HR CoEs to empower data-driven decisions concerning workforce strategy, talent acquisition, performance management, and employee engagement. The position reports to a People Analytics Solutions Lead and requires strong technical expertise in HR data engineering, working with large HR datasets, and demonstrated knowledge of various HR domains (data, analytics, and processes).

What's in it for you? You will have the opportunity to influence our talent strategy, design the People Analytics data infrastructure stack, develop impactful insights, and be part of a team that prioritizes innovation and curiosity.

Work Arrangement

This role is based remotely. However, if you live within a 50-mile radius of Atlanta (GA), Austin (TX), Detroit (MI), Warren (MI), or Mountain View (CA), you are expected to report to that location a minimum of three times a week.

Responsibilities

• Design, develop, and maintain ETL/ELT processes for HR data from multiple systems, including Workday, to support data-driven decision-making.
• Drive the implementation of robust HR data models and pipelines optimized for reporting and analytics, ensuring data quality, reliability, and security for on-prem and Azure cloud solutions.
• Develop pipelines and testing automation to ensure HR data quality and integrity across multiple data sources.
• Collaborate with People Analytics and HR business partners to understand data requirements and deliver reliable solutions.
• Partner with technical teams to build a best-in-class data environment and technology stack for People Analytics teams.
• Ensure data integrity, quality, consistency, security, and compliance (e.g., GDPR, CCPA, HIPAA where applicable).
• Design and implement secure processes for handling sensitive information within our data tech stack, maintaining appropriate access controls and confidentiality.
• Automate manual HR reporting and improve data accessibility through scalable data pipelines across the entire HR employee lifecycle.
• Troubleshoot and resolve data-related issues quickly and efficiently.
• Contribute to HR tech stack evaluations and migrations, with a focus on data capabilities and API integrations.
• Incorporate external data sources into internal datasets for comprehensive analysis.
• Manage and optimize platform architecture, including Databricks environment configuration and performance optimization.
• Stay current with emerging trends and advancements in data engineering, both technically and within the HR and People Analytics/sciences domain.

Requirements

• 5+ years of experience as an HR Data Engineer, leading HR data engineering transformations and implementing data pipelines and data solutions within the People Analytics/HR domain.
• Very good understanding of HR data and HR employee lifecycle processes (talent acquisition, talent development, workforce planning, engagement, employee listening, external benchmarking, etc.).
• Very good understanding of HCM data architecture, models, and data pipelines.
• Experience designing and implementing data integrations and ETLs with Workday (RaaS, APIs).
• Experience designing and automating data and analytics solutions that provide insights and recommendations at scale.
• Proficiency in SQL, R/Python, and ETL tools.
• Deep expertise in data modeling, data warehousing, and data pipeline development.