About the Team: 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
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.
- 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.
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 domains.
- Experience operating in regulated environments with strong data compliance and audit requirements.
The pay range for this position is $145,600 to $209,300. The actual base pay offered may vary depending on skills, experience, job-related knowledge and work location. In addition to base pay, employees may be eligible to participate in a performance-based bonus plan and to receive restricted stock unit awards as part of total compensation. Learn more about UKG's benefits and rewards at https://www.ukg.com/about-us/careers/benefits
