Full Time Data Analytics 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 full time data analytics engineer jobs pay per year?
As of Aug 24, 2026, the average yearly pay for full time data analytics 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.
A Full Time Data Analytics Engineer is responsible for designing, building, and maintaining systems that collect, process, and analyze large volumes of data. They work closely with data scientists and business stakeholders to develop pipelines and tools that transform raw data into meaningful insights. Their work involves data modeling, database management, and the use of analytics tools to help organizations make data-driven decisions. Additionally, they ensure data quality, security, and scalability within the analytics infrastructure.
To thrive as a Full Time Data Analytics Engineer, you need strong analytical skills, proficiency in statistics, and a background in computer science or a related field, usually supported by a relevant degree. Familiarity with tools such as SQL, Python, R, data visualization platforms (like Tableau or Power BI), and experience with cloud data systems or certifications (e.g., AWS, Google Cloud) are typical requirements. Exceptional problem-solving abilities, communication skills, and attention to detail help you interpret complex data and collaborate effectively with stakeholders. These skills are essential to transform raw data into actionable insights, driving business decisions and organizational growth.
Full Time Data Analytics Engineers often encounter challenges when integrating and cleaning data from multiple, disparate sources, such as databases, APIs, and flat files. Ensuring data quality, consistency, and compatibility across these sources can be complex and time-consuming. Additionally, the need to collaborate with both technical and non-technical stakeholders to understand data requirements and business objectives requires strong communication skills. Addressing these challenges is critical for delivering accurate and actionable insights.
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