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.
PRIVACY POLICY
Skills Required
- Bachelor's degree in Computer Science or a related degree with computer coursework, or equivalent experience
- At least five years of experience running NoSQL databases, including MongoDB and Redis
- Experience running database workloads on Microsoft Azure or similar cloud providers
- Proficiency with scripting and automation tools such as PowerShell, Python, Ansible, or Terraform
- Expertise in database automation, monitoring, and performance engineering
- Familiarity with database monitoring tools such as Prometheus, Grafana, OpenTelemetry, or Azure Monitoring
- Strong documentation and diagramming skills
- Experience with database clustering and replication
- Strong judgment, analytical, and problem-solving skills
- Ability to work with colleagues and stakeholders at various organizational levels
- QSR experience
- Linux systems administration experience
- Relevant certification in IAM
- Ability to interact with vendor APIs for automation
Little Caesars Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Little Caesars and has not been reviewed or approved by Little Caesars.
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Healthcare Strength — Full-time managers and above receive comprehensive medical, dental, vision, life, and disability coverage. Employees meeting full-time eligibility thresholds are also offered medical coverage.
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Retirement Support — Full-time managers have access to a 401(k) with company match. This retirement benefit accompanies the broader management-level package.
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Strong & Reliable Incentives — Performance bonuses are tied to sales increases, cost decreases, and customer satisfaction.
Little Caesars Insights
What We Do
ABOUT LITTLE CAESARS® Headquartered in Detroit, Michigan, Little Caesars was founded by Mike and Marian Ilitch in 1959 as a single, family-owned restaurant. Today, Little Caesars is the third largest pizza chain in the world, with stores in each of the 50 U.S. states and 27 countries and territories. Little Caesars recently introduced contactless options for both delivery and carry-out through the Little Caesars app. Pizzas are baked in 475-degree ovens to ensure food safety and never touched after baking. The chain has also reinforced cleanliness and sanitization procedures, increasing the frequency of cleaning commonly touched surfaces including door handles, glass, countertops, Pizza Portal surfaces, phones, and cash registers. Known for its HOT-N-READY® pizza and famed Crazy Bread®, Little Caesars has been named “Best Value in America” for the past twelve years (based on nationwide survey of national quick service restaurant customers conducted by Sandelman & Associates - 2007-2019 entitled “Highest Rated Chain – Value for the Money”). Little Caesars products are made with quality ingredients, like fresh, never frozen, mozzarella and Muenster cheese and sauce made from fresh-packed, vine-ripened California crushed tomatoes. An exceptionally high growth company with 60 years of experience in the $145 billion worldwide pizza industry, Little Caesars is continually looking for franchisee candidates to join our team in markets around the world. In addition to providing the opportunity for entrepreneurial independence in a franchise system, Little Caesars offers strong brand awareness with one of the most recognized and appealing characters in the country, Little Caesar
