Data Engineer Hybrid 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 data engineer hybrid jobs pay per year?
As of Aug 21, 2026, the average yearly pay for data engineer hybrid 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 Data Engineer Hybrid is a professional who designs, builds, and manages data infrastructure, and works both on-site and remotely. They are responsible for creating data pipelines, maintaining databases, and ensuring data quality and accessibility for analytics or business needs. The 'hybrid' aspect refers to their flexible work environment, combining office and remote work. Data Engineer Hybrids often collaborate closely with data scientists, analysts, and other IT professionals to enable effective data-driven decision-making.
To thrive as a Data Engineer Hybrid, you need strong programming skills (such as Python or Java), expertise in database management, and a solid understanding of data architecture and ETL processes, usually backed by a degree in computer science or a related field. Familiarity with cloud platforms (like AWS, Azure, or GCP), data warehousing tools (such as Snowflake or Redshift), and certifications in big data or cloud technologies are highly valuable. Analytical thinking, problem-solving, and effective communication help you collaborate across teams and translate business needs into technical solutions. These skills are crucial for building scalable, reliable data pipelines that support organizational decision-making and data-driven strategy.
In a hybrid Data Engineer role, team collaboration often involves a mix of remote and in-person interactions, which can enhance flexibility but also requires clear communication and effective use of collaboration tools. Data Engineers typically coordinate closely with data scientists, analysts, and IT teams to design and maintain data pipelines, regardless of work location. Project delivery may require scheduled on-site meetings for brainstorming or troubleshooting, but much of the individual development work can be completed remotely. Adapting to this environment means being proactive in communication and leveraging project management platforms to ensure alignment and timely delivery.
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