Engineering Data Analyst information
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$43.3K - $52.5K
7% of jobs
$52.5K - $61.8K
13% of jobs
$62.4K is the 25th percentile. Wages below this are outliers.
$61.8K - $71.1K
18% of jobs
The median wage is $75.4K / yr.
$71.1K - $80.4K
16% of jobs
$80.4K - $89.6K
13% of jobs
$93.1K is the 75th percentile. Wages above this are outliers.
$89.6K - $98.9K
9% of jobs
$98.9K - $108.2K
5% of jobs
$108.2K - $117.5K
9% of jobs
$117.5K - $126.7K
3% of jobs
$126.7K - $136K
2% of jobs
How much do engineering data analyst jobs pay per year?
As of Sep 9, 2026, the average yearly pay for engineering data analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.
An Engineering Data Analyst is a professional who uses data analysis techniques to support engineering projects and decision-making. They collect, clean, and interpret large sets of engineering data, often using specialized software tools. Their work helps identify trends, optimize processes, and improve the efficiency and quality of engineering solutions. They often collaborate with engineers and project managers to turn data insights into actionable recommendations.
To thrive as an Engineering Data Analyst, you need strong analytical skills, proficiency in mathematics and statistics, and a background in engineering or a related quantitative field. Familiarity with data analysis tools such as Python, R, SQL, and visualization platforms like Tableau or Power BI, along with relevant certifications, is often required. Attention to detail, problem-solving ability, and effective communication help you interpret data and translate findings for technical and non-technical stakeholders. These skills are crucial for transforming complex engineering data into actionable insights that drive process improvements and innovation.
Engineering Data Analysts often collaborate with teams such as software engineers, project managers, and product designers. A common challenge is bridging the gap between complex analytical findings and actionable insights for non-technical colleagues. Clear communication and the ability to translate data into practical recommendations are essential. Additionally, managing multiple priorities and ensuring data integrity across various sources can be demanding but is crucial for successful project outcomes.
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