Senior Etl Engineer information
See salary details
$59.5K - $70.8K
1% of jobs
$104.4K is the 25th percentile. Wages below this are outliers.
$93.3K - $104.6K
14% of jobs
$104.6K - $115.9K
17% of jobs
The median wage is $120.6K / yr.
$115.9K - $127.1K
19% of jobs
$127.1K - $138.4K
13% of jobs
$139.8K is the 75th percentile. Wages above this are outliers.
$138.4K - $149.7K
11% of jobs
$149.7K - $161K
7% of jobs
$161K - $172.2K
5% of jobs
$172.2K - $183.5K
3% of jobs
How much do senior etl engineer jobs pay per year?
As of Aug 23, 2026, the average yearly pay for senior etl engineer in the United States is $126,557.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,500.00 and $143,500.00 per year, depending on experience, location, and employer.
Senior ETL Engineers are experienced professionals who design, develop, and maintain ETL (Extract, Transform, Load) processes that move and transform data from various sources into data warehouses or other storage systems. They play a critical role in ensuring data quality, optimizing data flows, and supporting business intelligence initiatives. Senior ETL Engineers often lead projects, mentor junior staff, and collaborate with data architects and analysts to deliver reliable data solutions for organizations.
To thrive as a Senior ETL Engineer, you need advanced knowledge of data integration, database design, and ETL processes, usually supported by a degree in computer science or a related field. Expertise with ETL tools like Informatica, Talend, or SSIS, as well as proficiency in SQL and relevant data warehousing platforms, is typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help manage complex data workflows and collaborate with cross-functional teams. These skills and qualities are crucial for ensuring reliable, scalable, and high-quality data pipelines that support business intelligence and analytics initiatives.
Senior ETL Engineers often encounter challenges integrating modern data pipelines with legacy systems, such as outdated databases or proprietary formats. These systems may lack proper documentation and have performance limitations, making data extraction and transformation more complex. Overcoming these hurdles requires strong problem-solving skills, effective communication with stakeholders, and sometimes, creative workarounds to ensure data accuracy and reliability while minimizing disruptions to existing processes.
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