Sr. Staff Software Engineer- Eng

UKG•Atlanta, GA

1d•$145,600 - $204,200

About The Position

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.

Requirements

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
Ability to leverage AI tools 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.
Ability to break ambiguous problems into structured designs and actionable plans.
Demonstrated ability to mentor engineers and influence technical direction across teams.
Deep experience building distributed data systems using GCP services (AWS is also acceptable), including BigQuery, Dataflow (Beam), Dataform, and Pub/Sub.
Strong understanding of data modeling, partitioning strategies, schema evolution, and cost-efficient query optimization.
Experience designing security-first data pipelines with privacy-preserving transformations.
Strong experience integrating data-science workflows or ML/analytical models into production systems.

Nice To Haves

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 domains.
Experience operating in regulated environments with strong data compliance and audit requirements.

Responsibilities

Mentor engineers; guide design reviews, coding standards, and technical best practices.
Translate analytical requirements into technical designs and execution plans in partnership with product and data stakeholders.
Troubleshoot complex issues involving distributed processing, data-quality anomalies, or system bottlenecks.
Lead architecture reviews, performance tuning, cost optimization, and reliability engineering initiatives.
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).
Build scalable data ingestion, transformation, and aggregation pipelines that support high-volume HCM datasets.
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
Introduce new GCP tools or frameworks as needed to support long-term scalability and maintainability.