Big Data Operations Engineer information
See salary details
$15.87 - $22.44
1% of jobs
$22.44 - $29.02
0% of jobs
$29.02 - $35.60
2% of jobs
$35.60 - $42.18
1% of jobs
$42.18 - $48.75
5% of jobs
$54.46 is the 25th percentile. Wages below this are outliers.
$48.75 - $55.33
18% of jobs
$55.33 - $61.91
20% of jobs
The median wage is $62.54 / hr.
$61.91 - $68.49
27% of jobs
$68.62 is the 75th percentile. Wages above this are outliers.
$68.49 - $75.07
13% of jobs
$75.07 - $81.64
8% of jobs
$81.64 - $88.22
4% of jobs
How much do big data operations engineer jobs pay per hour?
As of Sep 10, 2026, the average hourly pay for big data operations engineer in the United States is $62.98, according to ZipRecruiter salary data. Most workers in this role earn between $53.61 and $70.91 per hour, depending on experience, location, and employer.
A Big Data Operations Engineer is responsible for managing, monitoring, and optimizing large-scale data processing systems and infrastructure. They ensure that data pipelines, storage, and processing frameworks like Hadoop, Spark, or Kafka run smoothly and efficiently. Their duties often include troubleshooting issues, automating workflows, configuring clusters, and collaborating with data engineers and analysts to support data-driven applications. They play a crucial role in maintaining the reliability, scalability, and performance of big data environments.
To thrive as a Big Data Operations Engineer, you need strong skills in data engineering, distributed systems, and scripting languages, typically supported by a degree in computer science or related field. Familiarity with big data tools such as Hadoop, Spark, Kafka, and cloud platforms, as well as experience with monitoring and automation systems, is essential. Problem-solving, attention to detail, and effective communication are standout soft skills for this role. These skills ensure the reliable operation, scalability, and performance of big data infrastructures critical for data-driven organizations.
Big Data Operations Engineers often encounter challenges such as ensuring the reliability and scalability of data pipelines, managing real-time data ingestion, and troubleshooting performance bottlenecks. They must also address data quality issues and coordinate with development and analytics teams to minimize downtime during deployments or upgrades. Proactive monitoring, automation, and collaboration with cross-functional teams are essential to overcoming these challenges and maintaining seamless data operations.
What are popular job titles related to Big Data Operations Engineer jobs?
For Big Data Operations Engineer jobs, the most frequently searched job titles are:
