Salary for Mid-level / Intermediate Data Infrastructure Engineer in United States during 2024

Salary details

The average mid-level / intermediate Data Infrastructure Engineer salary lies between USD 150,000 and USD 220,000 in the United States. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Nov. 17, 2024

Top 20 Job Tags for Mid-level / Intermediate Data Infrastructure Engineer roles

The three most common job tag items assiciated with mid-level / intermediate Data Infrastructure Engineer job listings are Python, Engineering and Security. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

Python | 12 jobs Engineering | 12 jobs Security | 10 jobs Pipelines | 10 jobs SQL | 9 jobs Data pipelines | 9 jobs Architecture | 9 jobs AWS | 8 jobs Big Data | 7 jobs Kubernetes | 7 jobs Kafka | 6 jobs Azure | 6 jobs Data quality | 6 jobs Spark | 5 jobs Docker | 5 jobs Airflow | 4 jobs Terraform | 4 jobs Computer Science | 4 jobs CI/CD | 4 jobs Data governance | 4 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate Data Infrastructure Engineer roles

The three most common job benefits and perks assiciated with mid-level / intermediate Data Infrastructure Engineer job listings are Career development, Equity / stock options and Team events. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 8 jobs Equity / stock options | 5 jobs Team events | 5 jobs Startup environment | 4 jobs Competitive pay | 4 jobs Gear | 3 jobs Parental leave | 2 jobs Flex hours | 2 jobs Health care | 2 jobs Wellness | 1 jobs Relocation support | 1 jobs Medical leave | 1 jobs Salary bonus | 1 jobs Home office stipend | 1 jobs Unlimited paid time off | 1 jobs

Salary Composition

In the United States, the salary composition for a Mid-level Data Infrastructure Engineer in AI/ML/Data Science typically includes a combination of base salary, bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the largest portion, ranging from 70% to 85% of the total compensation package. Bonuses, which can be performance-based or company-wide, usually account for 10% to 20%. Additional remuneration, such as stock options, is more common in larger tech companies or startups and can make up 5% to 15% of the total package. The exact composition can vary significantly depending on the region, industry, and company size. For instance, tech hubs like Silicon Valley or New York City might offer higher base salaries and more substantial equity components compared to other regions.

Increasing Salary Further

To increase your salary beyond the median of USD 195,000, consider the following strategies:

• Specialization: Develop expertise in niche areas within data infrastructure, such as cloud computing, big data technologies, or AI/ML integration, which are in high demand.
• Leadership Roles: Transition into roles with more responsibility, such as a Lead Data Engineer or Data Infrastructure Manager, which typically offer higher compensation.
• Continuous Learning: Stay updated with the latest technologies and methodologies in data infrastructure. Pursuing advanced certifications or a master's degree can also enhance your qualifications.
• Networking: Engage with professional networks and communities to learn about new opportunities and trends in the industry.
• Negotiation Skills: Improve your negotiation skills to better advocate for higher compensation during job offers or performance reviews.

Educational Requirements

Most mid-level data infrastructure engineering positions require at least a bachelor's degree in computer science, information technology, data science, or a related field. Some employers may prefer candidates with a master's degree, especially for roles that involve complex data systems or advanced AI/ML applications. A strong foundation in mathematics, statistics, and programming is essential, as these skills are critical for designing and managing data infrastructure.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise to potential employers. Some valuable certifications include:

• AWS Certified Big Data – Specialty: Validates your ability to design and implement AWS services to derive value from data.
• Google Professional Data Engineer: Demonstrates proficiency in designing, building, and operationalizing data processing systems on Google Cloud Platform.
• Microsoft Certified: Azure Data Engineer Associate: Focuses on integrating, transforming, and consolidating data from various structured and unstructured data systems into structures suitable for building analytics solutions.
• Cloudera Certified Data Engineer: Recognizes skills in data ingestion, transformation, and storage using Cloudera's platform.

Required Experience

Typically, a mid-level data infrastructure engineer is expected to have 3 to 5 years of relevant experience. This experience should include hands-on work with data infrastructure technologies, such as Hadoop, Spark, Kafka, and cloud platforms like AWS, Azure, or Google Cloud. Experience in designing and optimizing data pipelines, as well as a solid understanding of database management and data warehousing, is also crucial.

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