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.
Role Summary
We are seeking a Principal Data Engineer, ML/AI Platform to lead the design, development, and governance of scalable data solutions that power machine learning, AI, and GenAI use cases on our GCP 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 of Data 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 inference, feature generation, experimentation, and monitoring in cloud-native environments, preferably Google Cloud Platform.
Essential Duties & Responsibilities
Architect and Lead ML-Ready Data Solutions
- Design end-to-end data architectures on GCP using services such as BigQuery, GCS, Dataproc, Composer, Dataform, Data Fusion, and related cloud-native tooling.
- Build and standardize scalable batch and near-real-time data pipelines that support:
o model training datasets
o feature engineering workflows
o batch and inference use cases
o analytics and operational reporting
- Define reusable patterns for data ingestion, transformation, quality validation, metadata capture, lineage, and observability.
- Design data models optimized not for BI/reporting, but also for machine learning, AI, and GenAI workloads, including structured, semi-structured, and unstructured data.
- Establish patterns for CDC, ELT, distributed processing, and data product design in support of modern cloud platforms.
Partner Closely with Data Science and ML Engineering
- Collaborate with Data Scientists, ML Engineers, Data Architects, Platform Engineers, and Product Owners to 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 as Vertex AI, feature stores, model monitoring, and inference pipelines.
- Contribute to data patterns that support AI/GenAI use 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 a GCP-first data 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 using GitHub 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+ years of experience in data engineering, data platforms, or data warehousing, including significant experience with cloud-native architectures.
- Proven track record designing and implementing large-scale data pipelines and data platforms on GCP.
- Demonstrated experience working closely with Data Scientists and ML Engineers to support production ML/AI solutions.
- Hands-on experience delivering data pipelines for model training, feature engineering, inference, or ML monitoring workflows.
- Experience modernizing legacy data solutions into scalable cloud-native architectures.
Technical Proficiency
- Strong hands-on expertise with Python, SQL, Spark, and distributed data processing frameworks.
- Deep experience with GCP services such as BigQuery, GCS, Dataproc, Composer, Dataform, and Data Fusion.
- Strong understanding of BigQuery-centric design, optimization, and cost management.
- Solid understanding of data architecture patterns including ELT, CDC, data products, lakehouse concepts, and domain-oriented architectures.
- Practical familiarity with ML/AI platform concepts such as:
o Vertex AI
o feature stores
o experiment reproducibility
o model data lineage
o batch and inference data flows
o 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 supporting ML, AI, or GenAI solutions in production.
- Familiarity with Vertex AI, Kubernetes, Docker, or cloud-native orchestration patterns.
- Experience with data observability, governance, and quality frameworks at scale.
- Experience with feature 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 as Professional Data Engineer, Professional Cloud Architect, or Professional 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 [email protected]
