Jobs and Careers

Sr. Staff Software Engineer- Eng

United StatesRemotefull_timeVerifiedPosted 28 Apr 2026

💰 $209,300/yr($145,600/yr – $209,300/yr)

About the role

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.

About the Team:

This staff level role leads the technical strategy and execution for a large-scale data platform built on Google Cloud. The platform transforms extensive customer datasets into advanced analytical outputs and derived metrics. As the most technically skilled engineer on the team, this person drives architectural direction, mentors engineers, and ensures the platform is secure, scalable, and reliable. The role includes close collaboration with a dedicated Data Science team that provides statistical methods and analytical models, which this engineer will help integrate and operationalize.

About the Role

Architect and evolve a cloud-native data platform using tools like BigQuery, Dataflow, Dataform, Pub/Sub, Dataproc, Cloud Storage or AWS equivalent tools like redshift, S3, Athena, ect
Lead architecture reviews, performance tuning, cost optimization, and reliability engineering initiatives.
Mentor engineers; guide design reviews, coding standards, and technical best practices.
Build scalable data ingestion, transformation, and aggregation pipelines that support high-volume HCM datasets.
Collaborate with the Data Science team to operationalize their models, quantitative logic, and statistical frameworks—without owning statistical methodology design.
Implement end-to-end data workflows that generate aggregated insights and derived metrics at scale.
Engineer strong privacy, compliance, and anonymization mechanisms into all data pipelines (masking, thresholding, auditing).
Translate analytical requirements into technical designs and execution plans in partnership with product and data stakeholders.
Mentor engineers; guide design reviews, coding standards, and technical best practices.
Troubleshoot complex issues involving distributed processing, data-quality anomalies, or system bottlenecks.
Introduce new GCP tools or frameworks as needed to support long-term scalability and maintainability.

About You:

Basic Qualifications:

8+ years of software engineering experience, including 3+ years in lead or staff-level positions.
Strong programming skills in Python, Java, Scala, or similar languages used in data engineering.
Deep experience building distributed data systems using GCP services (AWS is also acceptable), including BigQuery, Dataflow (Beam), Dataform, and Pub/Sub.
Strong programming skills in Python, Java, Scala, or similar languages used in data engineering.
Leverage AI coding agents such as Claude Code or Codex to accelerate software development, including generating production-ready code, refactoring existing systems, debugging complex issues, and improving engineering throughput while maintaining code quality, security, and architectural standards.
Strong understanding of data modeling, partitioning strategies, schema evolution, and cost-efficient query optimization.
Experience designing security-first data pipelines with privacy-preserving transformations.
Ability to break ambiguous problems into structured designs and actionable plans.
Demonstrated ability to mentor engineers and influence technical direction across teams.
Strong experience integrating data-science workflows or ML/analytical models into production systems.

Preferred Qualifications:

Experience architecting enterprise data lakes, lakehouse platforms, or real-time streaming data systems.
Experience enabling machine learning workflows, including feature engineering platforms, model training pipelines, or model deployment infrastructure.
Experience working with large-scale structured and unstructured datasets in cloud-based environments.
Experience defining data strategy across multiple teams, including metadata management and data lifecycle practices.
Experience improving platform reliability through observability, data quality frameworks, and automated validation techniques.
Experience mentoring senior engineers, data engineers, or data scientists and influencing technical direction across d