Data Reliability Engineer at Little Caesars in Detroit, MI

Job description

Job Summary The Data Reliability Engineer is responsible for ensuring the quality, reliability, and operational health of enterprise data products, platforms, and AI-enabled solutions. This role owns the Data & Model Quality Framework, establishes data quality standards and monitoring practices, and partners across Data & Analytics to proactively identify issues, improve data trust, and maintain reliable, scalable data products for reporting, analytics, and business operations. What You Will Do: Data Quality & Reliability Serve as the technical owner of the enterprise Data & Model Quality Framework, establishing standards for monitoring, validation, reliability, SLAs/SLOs, and alerting for critical data products. Partner with technical and business teams to proactively identify issues, conduct root-cause analysis, and continuously improve the accuracy and reliability of enterprise reporting, analytics, machine learning models, and AI-enabled solutions. Platform Reliability & Operational Health Own the operational health and observability of enterprise data platforms by developing automated monitoring, alerting, and performance capabilities across pipelines, reporting, data and machine learning models, and platform services. Proactively identify anomalies, reduce operational risk, and improve reliability across the Data & Analytics ecosystem. Data Foundations & Governance Enablement Advance the organization's d ata f oundations strategy through the implementation of technical governance capabilities that improve transparency, discoverability, and trust in enterprise data. Establish and maintain metadata management, data lineage, catalog management, and asset governance practices that support data quality, business understanding, regulatory compliance, and AI readiness. Release Management & Quality Assurance Design and maintain testing, validation, and deployment processes that ensure enterprise data products are released with confidence. Establish automated quality controls, regression testing standards, and deployment governance practices that improve production stability, reduce release-related incidents, and support scalable delivery across reporting, analytics, machine learning, and AI solutions. Partner with Data Engineers and Analytics Engineers to incorporate quality, reliability, and operational readiness testing into development and deployment processes. Security Visibility & Access Governance Provide visibility into how enterprise data platforms are accessed, secured, and utilized. Develop monitoring and governance practices that support responsible access management, platform security, and compliance objectives while ensuring transparency into privileged roles, permissions, utilization patterns, and access models across enterprise data platforms. Continuous Improvement & Innovation Evaluate new technologies, tools, and approaches that improve data reliability, governance, monitoring, quality management, and operational effectiveness. Lead proof-of-concept efforts, provide recommendations supported by technical and business considerations, and contribute to roadmaps that advance the organization's Data Foundations and Data Reliability strategy. Role Expectations & Competencies The following competencies reflect the nature of the role and the environment in which it operates, rather than prescriptive minimum qualifications: Technical Problem Solving : The role requires investigating complex data issues across multiple systems, platforms, and technologies. Success depends on identifying root causes, evaluating alternatives, and implementing durable solutions that improve reliability and reduce operational risk. Dealing with Ambiguity : The role requires balancing competing priorities while making technical decisions in environments where information may be incomplete, evolving, or ambiguous. Decisions should consider scalability, maintainability, operational impact, and business value. Collaboration & Communication : Su