Api 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 api data engineer jobs pay per year?
As of Sep 9, 2026, the average yearly pay for api 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.
API Data Engineers are professionals who design, develop, and maintain systems that enable data to be accessed, transferred, or integrated via Application Programming Interfaces (APIs). They focus on building scalable and reliable data pipelines that connect different applications or services, ensuring smooth data flow and quality. Their work involves both backend engineering and a deep understanding of data structures, often collaborating with data scientists, analysts, and software developers to support business and analytics needs.
To thrive as an API Data Engineer, you need strong programming skills (often in Python, Java, or Node.js), expertise in data modeling, and experience with designing and managing APIs, typically supported by a degree in computer science or a related field. Familiarity with RESTful and GraphQL APIs, cloud platforms (such as AWS or Azure), and tools like Postman or Swagger, along with relevant certifications, is highly valuable. Excellent problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and translate requirements into scalable data solutions. These skills ensure efficient integration, reliable data pipelines, and robust API performance, which are crucial for modern data-driven applications.
API Data Engineers often encounter challenges such as data inconsistency, varying data formats, and differing authentication protocols when integrating data from multiple APIs. Managing rate limits, ensuring data quality, and maintaining data security during transfers are also frequent hurdles. To overcome these, engineers typically implement robust error handling, data validation routines, and use middleware or integration platforms to streamline data ingestion workflows.
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