Principal Data Engineer

Why UKG

At UKG, the work you do matters. The code you ship, the decisions you make, and the care you show a customer all add up to real impact. Today, tens of millions of workers start and end their days with our workforce operating platform. Helping people get paid, grow in their careers, and shape the future of their industries. That's what we do.

We never stop learning. We never stop challenging the norm. We push for better, and we celebrate the wins along the way. Here, you'll get flexibility that's real, benefits you can count on, and a team that succeeds together. Because at UKG, your work matters—and so do you.

Principal Data Engineer, ML/AI Platform (P5)

Role Summary

We are seeking aPrincipal Data Engineer, ML/AI Platformto lead the design, development, and governance of scalable data solutions that power machine learning, AI, and GenAI use cases on ourGCP Cloud Data Platform. This role will serve as a senior technical leader responsible for building robust data foundations for analytics, data science, and production ML systems.

The ideal candidate combines deep cloud data engineering expertise with practical experience supporting teams ofData Scientists and ML Engineers. They understand how to design reliable, high-quality, cost-efficient data pipelines and data products that enable model training, batch and online inference, feature generation, experimentation, and monitoring in cloud-native environments, preferablyGoogle Cloud Platform.

Essential Duties & Responsibilities

Architect and Lead ML-Ready Data Solutions

• Design end-to-end data architectures on GCP using services such asBigQuery, GCS, Dataproc, Composer, Dataform, Data Fusion, and related cloud-native tooling.
• Build and standardize scalable batch and near-real-time data pipelines that support:
• model training datasets
• feature engineering workflows
• batch and online inference use cases
• analytics and operational reporting
• Define reusable patterns for data ingestion, transformation, quality validation, metadata capture, lineage, and observability.
• Design data models optimized not only for BI/reporting, but also formachine learning, AI, and GenAI workloads, including structured, semi-structured, and unstructured data.
• Establish patterns forCDC, ELT, distributed processing, and data product designin support of modern cloud platforms.
  
  

Partner Closely with Data Science and ML Engineering

• Collaborate withData Scientists, ML Engineers, Data Architects, Platform Engineers, and Product Ownersto translate business and model requirements into scalable data solutions.
• Enable ML teams by delivering trusted, discoverable, and reproducible datasets for experimentation and production.
• Support feature generation and data preparation workflows used in model development and operationalization.
• Partner with ML engineering teams on data interfaces for tools such asVertex AI, feature stores, model monitoring, and inference pipelines.
• Contribute to data patterns that supportAI/GenAIuse cases such as prompt pipelines, retrieval-augmented generation (RAG), vector-ready data preparation, and document/content processing where applicable.
  
  

Strategic Data Engineering Leadership

• Drive architecture direction for aGCP-firstdata platform serving analytics and AI/ML workloads.
• Evaluate emerging technologies and recommend best-fit solutions to improve scalability, performance, reliability, and cost efficiency.
• Lead modernization efforts to refactor legacy ETL into cloud-optimized, maintainable ELT and ML-ready data pipelines.
• Define best practices for data quality, lineage, governance, security, and lifecycle management across enterprise data assets used for analytics and ML.
  
  

Technical Leadership and Engineering Excellence

• Act as a technical mentor and reviewer for senior and mid-level data engineers.
• Lead large cross-functional initiatives and serve as the escalation point for complex data platform and production pipeline issues.
• Set coding, testing, and deployment standards usingGitHub and CI/CD practices.
• Promote strong software engineering discipline across the data engineering function, including code quality, automated testing, documentation, and operational readiness.
  
  

Operational Excellence

• Ensure reliability, scalability, observability, and governance across production data environments.
• Define and monitor SLAs/SLOs for critical data pipelines and data products.
• Create technical documentation, reusable frameworks, and metadata standards that improve enterprise data maturity.
• Partner with stakeholders to align data platform capabilities with business outcomes and ML/AI roadmap priorities.
  
  

Qualifications

Experience

• 10+ yearsof experience in data engineering, data platforms, or data warehousing, including significant experience withcloud-native architectures.
• Proven track record designing and implementing large-scale data pipelines and data platforms onGCP.
• Demonstrated experience working closely withData Scientists and ML Engineersto support production ML/AI solutions.
• Hands-on experience delivering data pipelines formodel training, feature engineering, inference, or ML monitoring workflows.
• Experience modernizing legacy data solutions into scalable cloud-native architectures.
  
  

Technical Proficiency

• Strong hands-on expertise withPython, SQL, Spark, and distributed data processing frameworks.
• Deep experience with GCP services such asBigQuery, GCS, Dataproc, Composer, Dataform, and Data Fusion.
• Strong understanding ofBigQuery-centricdesign, optimization, and cost management.
• Solid understanding of data architecture patterns includingELT, CDC, data products, lakehouse concepts, and domain-oriented architectures.
• Practical familiarity with ML/AI platform concepts such as:
• Vertex AI
• feature stores
• experiment reproducibility
• model data lineage
• batch and online inference data flows
• MLOps/DataOps practices
• Experience supporting structured, semi-structured, and unstructured data for AI/ML use cases.
  
  

Leadership and Collaboration

• Demonstrated success influencing architecture and engineering standards across multiple teams.
• Strong mentoring and technical leadership skills.
• Excellent communication and stakeholder management skills, with the ability to work across engineering, analytics, and data science teams.
  
  

Preferred

• Experience supportingML, AI, or GenAIsolutions in production.
• Familiarity withVertex AI, Kubernetes, Docker, or cloud-native orchestration patterns.
• Experience withdata observability, governance, and qualityframeworks at scale.
• Experience withfeature engineering platforms, vector-ready pipelines, or RAG/data preparation workflows.
• Experience integrating enterprise SaaS and operational data sources such as Salesforce, D365, Qualtrics, Pendo, or similar.
• Google Cloud certification such asProfessional Data Engineer,Professional Cloud Architect, orProfessional Machine Learning Engineer.
  
  

Company Overview

UKG is the Workforce Operating Platform that puts workforce understanding to work. With the world's largest collection of workforce insights, and people-first AI, our ability to reveal unseen ways to build trust, amplify productivity, and empower talent, is unmatched. It's this expertise that equips our customers with the intelligence to solve any challenge in any industry — because great organizations know their workforce is their competitive edge. Learn more at ukg.com.

UKG is proud to be an equal opportunity employer and is committed to promoting diversity and inclusion in the workplace, including the recruitment process.

Disability Accommodation in the Application and Interview Process

For individuals with disabilities that need additional assistance at any point in the application and interview process, please email [Confidential Information]