DataOps Engineer
DataOps Engineers apply DevOps principles to data pipelines. They build reliable, maintainable, and monitorable data infrastructure.
Median Salary
$155,000
Job Growth
High — data operations and monitoring are critical
Experience Level
Entry to Leadership
Salary Progression
| Experience Level | Annual Salary |
|---|---|
| Entry Level | $110,000 |
| Mid-Level (5-8 years) | $155,000 |
| Senior (8-12 years) | $190,000 |
| Leadership / Principal | $220,000+ |
What Does a DataOps Engineer Do?
DataOps Engineers build reliable, maintainable data pipelines and infrastructure. They set up orchestration systems that run data pipelines on schedule. They build monitoring and alerting to detect pipeline failures and data quality issues. They implement version control and testing for data pipelines. They design disaster recovery and backup strategies. They work on automation—reducing manual work. They partner with data teams to improve reliability and reduce toil.
A Typical Day
Monitoring: Check data pipeline monitoring dashboard. Fix alerts.
Debugging: Investigate failed pipeline. Debug root cause.
Quality checks: Implement data quality tests. Catch bad data early.
Infrastructure: Scale orchestration system. Add new compute capacity.
Testing: Implement tests for data pipelines. Catch bugs before production.
Documentation: Document pipeline dependencies and failure procedures.
Optimization: Optimize pipeline performance. Reduce costs.
Key Skills
Career Progression
DataOps engineers typically come from DevOps or data engineering backgrounds. Senior engineers lead data operations across organizations.
How to Get Started
DevOps fundamentals: CI/CD, infrastructure-as-code, monitoring, automation.
Data pipelines: Understand how data flows—sources, transformations, destinations.
Orchestration: Learn Airflow, Databricks, or similar. Build production pipelines.
Monitoring: Learn to monitor data pipelines. Detect and alert on issues.
Quality: Implement data quality testing and monitoring.
Testing: Learn testing for data pipelines.
Real experience: Work on production data systems.
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Explore Track →Frequently Asked Questions
What's DataOps and how is it different from DevOps?▼
DevOps is about deploying code. DataOps is about data pipelines. Both apply similar principles—automation, testing, monitoring.
What tools do DataOps engineers use?▼
Orchestration (Airflow, Databricks), monitoring (Great Expectations, Databand), transformation (dbt), testing frameworks, infrastructure-as-code.
What's a data quality issue and how do you prevent it?▼
Quality issues: missing data, outliers, schema violations, duplicates. Prevent with validation rules, monitoring, and automated tests.
How do you monitor data pipelines?▼
Monitor: pipeline execution time, failure rates, data volume, data quality metrics. Set alerts for anomalies.
What's the biggest operational challenge with data?▼
Data quality and reliability. Complex dependencies between pipelines. A failure upstream breaks everything downstream.
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Last updated: 2026-03-07