Salary for Senior-level / Expert Data Engineer in United States during 2023

Salary details

The average senior-level / expert Data Engineer salary lies between USD 124,000 and USD 190,000 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

Salary trend

Top 20 Job Tags for Senior-level / Expert Data Engineer roles

The three most common job tag items assiciated with senior-level / expert Data Engineer job listings are Python, Engineering and SQL. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:

Python | 3304 jobs Engineering | 3209 jobs SQL | 3062 jobs Pipelines | 2762 jobs AWS | 2324 jobs Spark | 2201 jobs Architecture | 2187 jobs Data pipelines | 2051 jobs ETL | 1996 jobs Big Data | 1735 jobs Computer Science | 1696 jobs Agile | 1650 jobs Java | 1525 jobs Azure | 1447 jobs Airflow | 1335 jobs Kafka | 1274 jobs Machine Learning | 1263 jobs Security | 1223 jobs Scala | 1184 jobs GCP | 1177 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert Data Engineer roles

The three most common job benefits and perks assiciated with senior-level / expert Data Engineer job listings are Career development, Health care and Flex hours. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:

Career development | 2566 jobs Health care | 1375 jobs Flex hours | 1294 jobs Startup environment | 1121 jobs Team events | 915 jobs Flex vacation | 861 jobs Competitive pay | 821 jobs Equity / stock options | 780 jobs Parental leave | 706 jobs Salary bonus | 703 jobs Insurance | 688 jobs Medical leave | 506 jobs Wellness | 482 jobs 401(k) matching | 358 jobs Home office stipend | 246 jobs Fitness / gym | 195 jobs Unlimited paid time off | 184 jobs Gear | 141 jobs Conferences | 116 jobs Relocation support | 103 jobs

Salary Composition for Senior Data Engineers

The salary for a Senior Data Engineer in the United States typically comprises several components: a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary is the largest component, often accounting for 70-80% of the total compensation package. Bonuses can vary significantly depending on the company and industry, ranging from 10-20% of the base salary. In tech hubs like Silicon Valley, Seattle, or New York, companies might offer substantial equity packages, which can be a significant part of the total compensation, especially in startups or high-growth companies. Larger companies might offer more stable bonuses and benefits, while smaller companies might compensate with higher equity stakes.

Steps to Increase Salary

To increase your salary beyond the median level, consider the following strategies:

• Specialize in High-Demand Skills: Focus on acquiring expertise in niche areas such as machine learning engineering, big data technologies, or cloud computing. Skills in platforms like AWS, Azure, or Google Cloud can be particularly lucrative.
• Pursue Leadership Roles: Transitioning into a lead or managerial role can significantly boost your earning potential. This might involve managing a team of data engineers or overseeing large-scale projects.
• Negotiate Effectively: When changing jobs or during performance reviews, negotiate for higher pay by leveraging your experience, skills, and the value you bring to the company.
• Continuous Learning: Stay updated with the latest technologies and trends in data engineering. Attending workshops, conferences, and enrolling in advanced courses can enhance your skill set and marketability.

Educational Requirements

Most senior data engineering roles require at least a bachelor's degree in computer science, information technology, engineering, or a related field. However, a master's degree or even a Ph.D. can be advantageous, particularly in competitive markets or for roles that require a deep understanding of complex data systems. Advanced degrees can also provide a pathway to more specialized roles or leadership positions.

Helpful Certifications

Certifications can bolster your credentials and demonstrate expertise in specific areas. Some valuable certifications include:

• AWS Certified Big Data – Specialty: Validates expertise in using AWS data services.
• Google Professional Data Engineer: Demonstrates proficiency in designing, building, and operationalizing data processing systems.
• Microsoft Certified: Azure Data Engineer Associate: Focuses on implementing data solutions using Azure services.
• Cloudera Certified Professional (CCP) Data Engineer: Recognizes skills in data ingestion, transformation, and storage.

Experience Requirements

Typically, a senior data engineer is expected to have 5-10 years of experience in data engineering or related fields. This experience should include a strong background in data architecture, ETL processes, data warehousing, and proficiency in programming languages such as Python, Java, or Scala. Experience with big data technologies like Hadoop, Spark, and Kafka is often required, along with familiarity with cloud platforms.

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