Principal Data Engineer, People Data

What you’ll be doing

• Design, build and evolve high scale data solutions that serve as the reference architecture for the organization and simplify data access across the organization
• Develop data quality frameworks, monitoring, anomaly detection, and alerting, with governance, lineage tracking, and change management rigor appropriate for externally reported numbers
• Drive adoption of consistent data modeling patterns, naming conventions, documentation norms, and metric governance standards across the data organization
• Lead cross-functional technical initiatives across data verticals to accelerate delivery and harden data systems
• Navigate ambiguity and make sound technical decisions, balancing short-term delivery with long-term infrastructure investment
• Champion Data Contracts: implement data contracts and schema evolution practices to ensure reliability across global product teams
• Mentor and Scale: Raise the technical bar by mentoring senior engineers, leading cross functional task forces, and fostering a culture of engineering excellence and data driven decision making
• Stay at the forefront of the data landscape, adopting and advocating for emerging technologies and practices to guide the team’s long-term technical direction

What you’ll bring to the role

• 10+ years in a data engineering role, with proven experience in a senior or lead capacity. Experience with Federal government compliance and security requirements is highly desirable.
• Expert-level experience with SQL, ETL/ELT tools (Airflow, dbt), and MPP databases (Snowflake, Redshift). Extensive hands-on experience with AWS services (S3, Lambda, EMR, ECR, EKS).
• Expertise in HR data - specifically in candidate lifecycle, employee records, compensation, and organizational data. Experience designing data models that capture recruiting analytics, engagement signals, and workforce planning metrics.
• Familiarity with Workday data structures is a bonus
• Experience building and maintaining batch and realtime pipelines using technologies like Spark, Kafka
• Deep experience designing and working with modern lakehouse and warehouse architectures (Databricks, Snowflake) and modern file formats (Iceberg, Delta)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Track record of hands-on collaboration with Data Science, Business, and Product teams