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

Salary details

The average senior-level / expert Analytics Engineer salary lies between USD 130,000 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: Dec. 22, 2024

Salary trend

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

The three most common job tag items assiciated with senior-level / expert Analytics Engineer job listings are SQL, Engineering and Python. 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:

SQL | 347 jobs Engineering | 336 jobs Python | 264 jobs Pipelines | 212 jobs Looker | 186 jobs Snowflake | 174 jobs Tableau | 167 jobs ETL | 149 jobs Data warehouse | 147 jobs Testing | 145 jobs Data pipelines | 137 jobs Airflow | 129 jobs Data Analytics | 129 jobs Architecture | 128 jobs dbt | 116 jobs Data quality | 114 jobs Computer Science | 108 jobs Redshift | 105 jobs AWS | 102 jobs BigQuery | 99 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Analytics Engineer job listings are Career development, Health care and Startup environment. 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 | 285 jobs Health care | 206 jobs Startup environment | 161 jobs Flex vacation | 156 jobs Equity / stock options | 144 jobs Flex hours | 141 jobs Team events | 121 jobs Salary bonus | 121 jobs Competitive pay | 103 jobs Insurance | 91 jobs Medical leave | 87 jobs Parental leave | 86 jobs 401(k) matching | 61 jobs Wellness | 52 jobs Fitness / gym | 49 jobs Gear | 48 jobs Home office stipend | 47 jobs Unlimited paid time off | 33 jobs Conferences | 26 jobs Fertility benefits | 18 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Analytics Engineer in AI/ML/Data Science typically includes a combination of a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the majority of the total compensation package, ranging from 70% to 85%. Bonuses can vary significantly depending on the company and industry, often ranging from 10% to 20% of the base salary. Additional remuneration, such as stock options, is more common in larger tech companies and startups, potentially adding another 5% to 15% to the total compensation. Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York City tend to be higher due to the cost of living and competitive job market.

Increasing Salary

To increase your salary further from a Senior-level position, consider the following strategies:

• Specialization: Develop expertise in a niche area of AI/ML, such as natural language processing, computer vision, or reinforcement learning, which can make you more valuable to employers.
• Leadership Roles: Transition into leadership or managerial roles, such as a Director of Data Science or Chief Data Officer, which typically offer higher compensation.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML through courses, workshops, and conferences.
• Networking: Build a strong professional network to learn about higher-paying opportunities and gain insights into industry trends.
• Negotiation Skills: Improve your negotiation skills to better advocate for higher compensation during job offers or performance reviews.

Educational Requirements

Most Senior-level Analytics Engineer positions require at least a bachelor's degree in a relevant field such as Computer Science, Data Science, Statistics, or Mathematics. However, a master's degree or Ph.D. is often preferred, especially for roles that involve complex problem-solving and advanced analytical techniques. Advanced degrees can provide a deeper understanding of machine learning algorithms, statistical models, and data analysis, which are crucial for high-level positions.

Helpful Certifications

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

• Certified Analytics Professional (CAP)
• Google Professional Machine Learning Engineer
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure Data Scientist Associate
• TensorFlow Developer Certificate

These certifications can validate your skills in specific tools and platforms, making you a more attractive candidate for senior roles.

Required Experience

Typically, a Senior-level Analytics Engineer is expected to have at least 5 to 10 years of experience in data science, machine learning, or a related field. This experience should include hands-on work with data analysis, model development, and deployment, as well as experience with programming languages such as Python or R. Experience in leading projects, mentoring junior team members, and collaborating with cross-functional teams is also highly valued.

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