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 data foundations 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: Success depends on working effectively across Data Governance, Data Engineering, Analytics Engineering, Data Architecture, Business Intelligence, IT, and business stakeholders. The role regularly communicates technical concepts to audiences with varying levels of technical expertise.
- Platform & Operational Excellence: The role emphasizes disciplined engineering and operational practices, including monitoring, testing, automation, release management, observability, documentation, and continuous improvement.
- Continuous Learning & Innovation: The role requires staying current with evolving technologies, data management practices, AI capabilities, and engineering approaches. Success includes applying this knowledge to improve enterprise data reliability, operational efficiency, and business outcomes.
Who You Are:
- Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, Mathematics, or a related field. A minimum of four (4) years of relevant industry experience may be considered in lieu of a degree.
- Strong experience designing, building, and supporting enterprise data solutions in at least one of the following areas:
- Data engineering, ETL/ELT development, and data integration processes
- Cloud-based data platforms such as Databricks, Azure, Snowflake, or equivalent technologies
- Data quality monitoring, testing, validation, and observability frameworks
- Data modeling, semantic models, reporting platforms, and business intelligence solutions
- Metadata management, data cataloging, lineage, and governance technologies
- CI/CD, source control, automation, and deployment management processes
- Experience supporting production data environments and troubleshooting data quality, pipeline, or platform issues.
What Will Make You Stand Out:
- Experience with Databricks, Unity Catalog, and Azure Data Factory.
- Experience with Programming and scripting languages such as SQL, Python, or similar scripting languages
- Experience with Microsoft Purview or data catalog/metadata management platforms.
- Experience implementing data lineage solutions.
- Experience supporting AI governance, model governance, or responsible AI initiatives.
- Experience with Azure DevOps, Git, or similar DevOps platforms.
- Experience building automated data quality monitoring and alerting frameworks.
- Experience supporting Power BI semantic models and enterprise reporting solutions.
- Experience administrating Microsoft Fabric as a platform
- Experience monitoring machine learning models, including model performance, prediction quality, model drift, and operational health
- Experience establishing SLAs/SLOs and reliability standards for enterprise data products
Working Conditions
This position may include participation in production support activities and occasional work during nights, weekends, or holidays when supporting critical releases, production incidents, or operational events. Work is typically performed in a standard office or remote environment, with occasional travel to restaurant locations, company offices, or industry events as needed in accordance with company travel policies.
All items listed above are illustrative and not comprehensive. They are not contractual in nature and are subject to change at the discretion of Little Caesars Enterprises Inc.
Little Caesar Enterprises, Inc. is an Equal Employment Opportunity employer. All qualified applicants will receive consideration for employment without regards to that individual’s race, color, religion or creed, national origin or ancestry, sex (including pregnancy), sexual orientation, gender identity, age, physical or mental disability, veteran status, genetic information, ethnicity, citizenship, or any other characteristic protected by law. The Company will strive to provide reasonable accommodations to permit qualified applicants who have a need for an accommodation to participate in the hiring process (e.g., accommodations for a job interview) if so requested. This company participates in E-Verify. Click on any of the links below to view or print the full poster. E-Verify and Right to Work.
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