Salary for Executive-level / Director Platform Engineer in United States during 2024

Salary details

The average executive-level / director Platform Engineer salary lies between USD 151,600 and USD 227,400 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 Executive-level / Director Platform Engineer roles

The three most common job tag items assiciated with executive-level / director Platform Engineer job listings are Engineering, Architecture 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:

Engineering | 19 jobs Architecture | 14 jobs Security | 12 jobs Computer Science | 12 jobs Testing | 11 jobs Agile | 11 jobs Machine Learning | 9 jobs Kubernetes | 9 jobs Python | 8 jobs Azure | 8 jobs CI/CD | 8 jobs AWS | 7 jobs Finance | 7 jobs MLOps | 7 jobs Pipelines | 7 jobs LLMs | 7 jobs Generative AI | 7 jobs Scala | 6 jobs CX | 6 jobs SDLC | 6 jobs

Top 20 Job Perks/Benefits for Executive-level / Director Platform Engineer roles

The three most common job benefits and perks assiciated with executive-level / director Platform 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 2024 and the number of open jobs that where offering them during that period:

Career development | 20 jobs Health care | 12 jobs Startup environment | 10 jobs Competitive pay | 8 jobs Insurance | 7 jobs Equity / stock options | 5 jobs Flex hours | 5 jobs Flex vacation | 5 jobs Medical leave | 5 jobs Team events | 4 jobs Salary bonus | 4 jobs Wellness | 3 jobs Transparency | 3 jobs Parental leave | 2 jobs 401(k) matching | 1 jobs Home office stipend | 1 jobs Flexible spending account | 1 jobs

Salary Composition

In the United States, the salary for an Executive-level or Director Platform Engineer in AI/ML/Data Science typically comprises a base salary, performance 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 60% to 80%. Performance bonuses can vary significantly, often between 10% to 20% of the base salary, depending on the company's performance and individual achievements. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and may account for 10% to 30% of the total compensation. The exact composition can vary based on the region, with tech hubs like Silicon Valley offering higher equity components, and the industry, with finance and healthcare often providing higher base salaries. Company size also plays a role, with larger companies typically offering more comprehensive benefits and bonuses.

Increasing Salary Further

To increase your salary beyond the median of USD 170,250, consider pursuing roles with greater responsibility, such as VP of Engineering or Chief Technology Officer (CTO). Expanding your skill set to include emerging technologies and leadership capabilities can make you more valuable. Networking within industry circles and attending conferences can open up opportunities for higher-paying roles. Additionally, negotiating your compensation package by leveraging offers from other companies can be effective. Pursuing further education, such as an MBA or specialized executive programs, can also enhance your qualifications for higher-level positions.

Educational Requirements

Most Executive-level or Director Platform Engineer roles require at least a bachelor's degree in computer science, engineering, data science, or a related field. However, a master's degree or Ph.D. is often preferred, especially in larger organizations or more competitive industries. Advanced degrees can provide a deeper understanding of complex algorithms, data structures, and machine learning models, which are crucial for strategic decision-making in AI/ML projects.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credibility and demonstrate your expertise. Certifications such as Certified Data Scientist (CDS), AWS Certified Machine Learning – Specialty, and Google Professional Machine Learning Engineer are highly regarded. Additionally, certifications in project management, like PMP or Agile methodologies, can be beneficial, as they demonstrate your ability to lead and manage complex projects effectively.

Required Experience

Typically, candidates for this role are expected to have 10-15 years of experience in software engineering, data science, or a related field, with a significant portion of that time spent in leadership or managerial roles. Experience in leading cross-functional teams, managing large-scale projects, and a proven track record of successful AI/ML implementations are crucial. Experience in a specific industry, such as finance, healthcare, or technology, can also be advantageous, as it provides domain-specific insights.

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