What Is a Data Reliability Engineer and Why It Matters
As organizations handle more data than ever, a new role has emerged in analytics teams: the data reliability engineer. But what exactly do they do, and how can they help your organization maintain trustworthy data?
Just as DevOps revolutionized software development by combining engineering and operations in the late 2000s, data teams are undergoing a similar transformation. Modern analytics requires specialists who ensure not only data availability but also quality and reliability.
In response, the data reliability engineer role has appeared—a specialist who applies best practices from DevOps, site reliability engineering (SRE), and cloud operations to data pipelines and platforms. Companies that prioritize reliable data now rely on this emerging discipline to prevent downtime, improve observability, and accelerate data-driven decision-making.
ZippyOPS supports organizations in this transition by providing consulting, implementation, and managed services in DevOps, DevSecOps, DataOps, Cloud, Automated Ops, AIOps, MLOps, Microservices, Infrastructure, and Security. Learn more about ZippyOPS services here or explore our solutions here.
What Does a Data Reliability Engineer Do?
The primary responsibility of a data reliability engineer is to ensure high-quality data is readily available across an organization. Broken data pipelines are inevitable, but these engineers detect, resolve, and prevent issues before they affect business outcomes.
Data reliability engineers often:
- Implement data observability tools and continuous testing frameworks
- Monitor pipelines for freshness, volume, schema, and lineage issues
- Manage incident response and communicate impact to stakeholders
- Collaborate with data engineers, analysts, and DevOps teams to build scalable, reliable platforms
In many ways, they are the data team equivalent of site reliability engineers in software, bridging the gap between engineering, analytics, and operations.
ZippyOPS helps companies operationalize these practices by integrating automated monitoring, cloud infrastructure, and AI-enabled tooling into data platforms. Our clients benefit from improved reliability while reducing time-to-resolution for data issues. See some of our product solutions here or watch our educational resources on YouTube.
Skills Required for a Data Reliability Engineer
A strong candidate typically has a background in data engineering, analytics, or data science, along with expertise in:
- Programming languages: Python, SQL, Java
- Data workflow tools: dbt, Airflow
- Cloud platforms: AWS, GCP, Snowflake, Databricks
- Reliability frameworks: SLAs, SLOs, monitoring, CI/CD pipelines
In addition, communication skills are essential. Data reliability engineers regularly collaborate with cross-functional teams to ensure all stakeholders understand the impact of data quality issues.
Example Job Levels
- Junior Data Reliability Engineer: 3+ years of experience in data engineering, focuses on implementing monitoring and observability frameworks.
- Senior Data Reliability Engineer: 5–7+ years experience, responsible for system design, process improvements, and mentoring junior engineers.
- Manager: 10+ years experience, leading teams, developing strategy, and ensuring high standards for scalable and secure data systems.
Companies like DoorDash, Disney Streaming Services, and Equifax have already integrated these roles to strengthen their data pipelines and analytics capabilities.
Applying the Data Reliability Lifecycle
Many data teams adopt a data reliability lifecycle, which borrows concepts from DevOps to improve overall data quality. It includes three main stages:
1. Detect: Automated monitoring and alerts help teams identify data issues quickly.
2. Resolve: Engineers communicate incidents to stakeholders and take corrective actions efficiently.
3. Prevent: Historical learnings and pipeline adjustments reduce recurring issues and improve data trust.
For example, an e-commerce company may detect an accidental schema change in a daily sales table. By implementing the lifecycle, the team can quickly identify the issue, correct it, and prevent similar errors in the future.
ZippyOPS offers end-to-end support in AIOps, DataOps, and Cloud integration, helping organizations implement lifecycle automation and reliability monitoring across their data infrastructure.
Measuring the Success of Data Reliability Engineers
The success of a data reliability engineer is measured by:
- Data trust and adoption: High usage of data across teams indicates reliable datasets
- Data downtime: Calculated using the number of incidents, time-to-detection (TTD), and time-to-resolution (TTR)
- SLAs and SLOs: Formal agreements that define and measure data service quality
For example, Red Ventures uses SLA conversations to align data quality metrics with business outcomes, focusing on actionable insights for stakeholders.
According to Gartner, poor data quality can cost organizations millions per year, making these KPIs crucial for business success.
The Future of Data Reliability Engineering
Demand for data reliability engineers is growing rapidly as organizations depend more on analytics, AI, and machine learning. LinkedIn reports that site reliability engineering was one of the fastest-growing positions, and data reliability is likely following a similar trajectory.
With the right expertise, processes, and tools, companies can maintain high-quality, trustworthy data across increasingly complex data ecosystems. ZippyOPS helps enterprises embrace these emerging practices, providing consulting, implementation, and managed services for modern data operations.
For organizations ready to improve data reliability and reduce downtime, contact ZippyOPS at sales@zippyops.com.
