Sr. Staff Software Engineer- Eng

UKG•Atlanta, GA

1d•$145,600 - $209,300

About The Position

We are seeking a Senior Staff Software Engineer – Data Platforms to lead the technical direction of large-scale data and analytics systems supporting the Verify/Benchmark domain. This role owns the most complex and high-risk data problem spaces, drives cross-team data strategy, and is recognized as a subject matter expert in scalable data architecture, advanced analytics infrastructure, and secure data systems. You operate with broad direction, independently define objectives, and establish execution approaches that shape the long-term evolution of our data ecosystem. About the Role Remain hands-on in architecting and implementing business-critical data systems, including large-scale data pipelines, real-time and batch processing frameworks, feature engineering platforms, and data services supporting analytics and machine learning workloads. Define and drive the long-term data strategy across the platform, including data modeling standards, storage architecture, governance frameworks, and ML infrastructure. Develop innovative solutions for cross-functional challenges involving data reliability, scalability, privacy, and analytical performance. Lead high-visibility initiatives involving distributed data systems and advanced analytics capabilities. Align engineering, data science, product, and platform teams around shared architectural decisions and execution plans. Own end-to-end architecture for data platforms, including ingestion, transformation, storage, access layers, and model enablement infrastructure. Anticipate scale constraints, performance bottlenecks, data quality risks, regulatory requirements, and evolving analytical use cases. Address ambiguous and technically complex data challenges such as large-scale identity resolution, benchmarking methodologies, statistical validation, feature lifecycle management, and model reproducibility. Develop new architectural patterns where needed. Lead major programs that influence data maturity, analytics capabilities, and machine learning enablement across the organization. Advocate for investment in data infrastructure, tooling, and governance. Accountable for data platform reliability, integrity, and operational maturity. Establish measurable standards for data quality, observability, lineage tracking, and incident management. Lead post-incident reviews focused on systemic improvement. Define and uphold standards for data engineering and ML engineering practices, including automated data validation, schema governance, reproducibility, versioning, and secure data handling. Drive improvements in CI/CD for data pipelines, automated testing of data transformations, deployment of ML workflows, rollback strategies, and operational monitoring across distributed systems. Champion testing strategies for data systems, including data quality checks, statistical validation, performance benchmarking, load testing, and resilience testing for distributed processing environments. Provide architectural-level review for high-impact data and ML initiatives, ensuring scalability, maintainability, and alignment with long-term data strategy. Mentor senior engineers, data engineers, and data scientists. Shape technical leadership across the data domain and elevate architectural thinking within teams. Ensure that data models, architectural decisions, governance policies, and long-term technical strategies are clearly documented and maintained. Introduce advanced concepts in data engineering, analytics infrastructure, and machine learning enablement. Guide experimentation into production-ready, scalable data solutions that improve platform capability and insight generation

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 used in data engineering.
Demonstrated experience designing and delivering large-scale data platforms, distributed data processing systems, or machine learning infrastructure in a production environment.
Demonstrated experience leading cross-team technical initiatives involving data architecture, data modeling, and scalable data pipelines.
Demonstrated experience implementing data governance, security, and privacy controls within data platforms.
Demonstrated experience establishing engineering standards for data quality, testing, monitoring, and operational reliability.
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related quantitative field, or equivalent professional experience and formal training.

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

Remain hands-on in architecting and implementing business-critical data systems, including large-scale data pipelines, real-time and batch processing frameworks, feature engineering platforms, and data services supporting analytics and machine learning workloads.
Define and drive the long-term data strategy across the platform, including data modeling standards, storage architecture, governance frameworks, and ML infrastructure. Develop innovative solutions for cross-functional challenges involving data reliability, scalability, privacy, and analytical performance.
Lead high-visibility initiatives involving distributed data systems and advanced analytics capabilities. Align engineering, data science, product, and platform teams around shared architectural decisions and execution plans.
Own end-to-end architecture for data platforms, including ingestion, transformation, storage, access layers, and model enablement infrastructure. Anticipate scale constraints, performance bottlenecks, data quality risks, regulatory requirements, and evolving analytical use cases.
Address ambiguous and technically complex data challenges such as large-scale identity resolution, benchmarking methodologies, statistical validation, feature lifecycle management, and model reproducibility. Develop new architectural patterns where needed.
Lead major programs that influence data maturity, analytics capabilities, and machine learning enablement across the organization. Advocate for investment in data infrastructure, tooling, and governance.
Accountable for data platform reliability, integrity, and operational maturity. Establish measurable standards for data quality, observability, lineage tracking, and incident management. Lead post-incident reviews focused on systemic improvement.
Define and uphold standards for data engineering and ML engineering practices, including automated data validation, schema governance, reproducibility, versioning, and secure data handling.
Drive improvements in CI/CD for data pipelines, automated testing of data transformations, deployment of ML workflows, rollback strategies, and operational monitoring across distributed systems.
Champion testing strategies for data systems, including data quality checks, statistical validation, performance benchmarking, load testing, and resilience testing for distributed processing environments.
Provide architectural-level review for high-impact data and ML initiatives, ensuring scalability, maintainability, and alignment with long-term data strategy.
Mentor senior engineers, data engineers, and data scientists. Shape technical leadership across the data domain and elevate architectural thinking within teams.
Ensure that data models, architectural decisions, governance policies, and long-term technical strategies are clearly documented and maintained.
Introduce advanced concepts in data engineering, analytics infrastructure, and machine learning enablement. Guide experimentation into production-ready, scalable data solutions that improve platform capability and insight generation