Golang Data Engineer information
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$44.5K - $56.6K
0% of jobs
$56.6K - $68.7K
1% of jobs
$68.7K - $80.8K
3% of jobs
$80.8K - $92.9K
4% of jobs
$115.5K is the 25th percentile. Wages below this are outliers.
$105K - $117K
11% of jobs
$117K - $129.1K
12% of jobs
The median wage is $132.4K / yr.
$129.1K - $141.2K
45% of jobs
$141.2K - $153.3K
8% of jobs
$153.3K - $165.4K
5% of jobs
$165.4K - $177.5K
3% of jobs
How much do golang data engineer jobs pay per year?
As of Sep 11, 2026, the average yearly pay for golang data engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.
Golang Data Engineers are professionals who use the Go programming language (Golang) to design, build, and maintain data pipelines, databases, and processing systems. They work closely with large datasets, ensuring efficient data flow, storage, and retrieval for analytics or application needs. Their expertise in Golang allows them to create high-performance, scalable, and reliable solutions, often in cloud or distributed computing environments. Additionally, they may collaborate with data scientists and analysts to provide clean, accessible data for business intelligence and machine learning purposes.
Golang Data Engineers often work closely with data scientists to understand their data processing needs and ensure that infrastructure supports efficient analysis and model deployment. They also collaborate with backend and DevOps teams to integrate data pipelines with existing systems and maintain data quality, reliability, and scalability. Regular meetings, code reviews, and shared documentation are common collaboration practices, helping to align technical solutions with business goals and ensure smooth workflow across teams.
To thrive as a Golang Data Engineer, you need strong programming skills in Go, a solid grasp of data structures, algorithms, and experience with data pipelines or ETL processes, often supported by a degree in computer science or related field. Familiarity with tools such as Kafka, Spark, SQL/NoSQL databases, and cloud platforms like AWS or GCP, as well as experience with version control systems like Git, is crucial. Effective problem-solving, collaboration, and communication skills help you work efficiently within cross-functional teams and adapt to evolving data needs. These combined skills ensure robust, scalable data solutions that support business analytics and decision-making.
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