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Python can help you qualify for well-paid technology work, but knowing the language alone does not qualify you for most of these jobs. Employers typically want Python alongside skills such as software engineering, SQL, statistics, networking, or cloud infrastructure. For U.S. context, the figures below are occupation-wide wage benchmarks—not Python-specific pay or guaranteed starting salaries.
The five paths are software or backend engineering, data science and machine learning, cybersecurity, data engineering, and cloud or DevOps. They are not an official salary ranking: government data classify workers by occupation, and titles such as “machine-learning engineer” and “DevOps engineer” can span several categories.
How to read the salary figures
The Bureau of Labor Statistics (BLS) reports wages for occupations, not for a particular programming language. Its May 2024 median is the midpoint: half of workers in that occupation earned more and half earned less. It is not an estimate of what a new graduate or someone who has just finished a Python course will earn. Pay also varies with location, industry, seniority, employer, bonuses, and equity.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteFor comparison, the BLS reported a $133,080 median annual wage for software developers in May 2024, and more than $211,450 for the highest-paid 10%. It reported a $112,590 median for data scientists, with the highest-paid 10% above $194,410. Data-scientist employment is projected to grow 34% from 2024 to 2034; software developers, quality-assurance analysts, and testers together are projected to grow 15%. Projections describe an occupation’s outlook, not an individual’s likelihood of getting hired. See the [BLS software developers, QA analysts, and testers profile](https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm) and the [BLS data scientists profile](https://www.bls.gov/ooh/math/data-scientists.htm).
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Other salary figures below come from BLS occupational comparisons and use the closest available category. Do not compare them as though every job title were measured identically. For a location-specific estimate, consult [CareerOneStop’s wage data](https://cloudfront.careeronestop.org/Toolkit/StateAndLocal/Wages.aspx?dataview=&location=21157&national=True&soccode=151252), and check the geography and year shown.
1. Software engineer or backend developer
Software engineers build and maintain applications, services, and the systems behind them. Python is commonly used for web backends, APIs, automation services, data-processing systems, testing tools, and internal business software. Frameworks such as Django, FastAPI, and Flask can help build applications, but the job is about designing and operating reliable software—not merely writing scripts.
U.S. benchmark: Software developers had a BLS median annual wage of $133,080 in May 2024; the highest-paid 10% earned more than $211,450. These are occupation-wide figures, not Python-developer salaries or entry-level offers.
Learn next: Git, testing (for example, pytest), SQL and database design, HTTP and APIs, authentication, deployment, Docker, data structures, and system design. A useful portfolio project is an API-backed application with a database, authentication, automated tests, clear setup instructions, and a deployed version.
Good fit if: You like building products and solving general technical problems. This path has a broad range of roles, but “Python developer” is not a standardized occupation: junior web development, backend engineering, platform work, and senior architecture can have very different responsibilities and pay.
2. Data scientist or machine-learning engineer
Python is used to clean and analyze data, run experiments, train and evaluate models, and—in engineering roles—serve models inside production systems. Data science and machine-learning engineering overlap, but they are not interchangeable. Data scientists often focus on analysis, statistical methods, experiments, and communicating findings. Machine-learning engineers tend to put more emphasis on production software, model deployment, infrastructure, and reliability.
U.S. benchmark: The BLS reports a $112,590 median annual wage for data scientists in May 2024; the highest-paid 10% earned more than $194,410. It projects 34% employment growth for data scientists from 2024 to 2034. That data-scientist median should not be treated as a salary figure for every machine-learning engineer, whose work may fall under other occupational categories.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Learn next: Probability, statistics, linear algebra, SQL, data visualization, experimental design, model evaluation, and software engineering. Tools may include pandas, NumPy, scikit-learn, PyTorch or TensorFlow, and experiment-tracking and deployment tools. Build an end-to-end project that explains the data, cleaning choices, evaluation method, limitations, and how the model would be deployed and monitored. A notebook that reports a high accuracy score without explaining validation or data leakage is weak evidence of job readiness.
Good fit if: You enjoy mathematics, data, and testing ideas. Some data roles accept a range of educational backgrounds, but BLS lists a bachelor’s degree as typical entry-level education for data scientists; research-heavy roles may expect advanced study or substantial research experience.
3. Cybersecurity engineer or information security analyst
Security teams use Python to automate repetitive work, analyze logs, query security platforms, investigate incidents, test systems, and build internal tools. Python helps, but security work also depends on understanding how networks, operating systems, identities, and applications behave.
U.S. benchmark: BLS lists a $124,910 median annual wage for information security analysts in its computer-occupation comparisons. That is an occupation-level figure, not a Python-specific or necessarily engineering-title salary. BLS describes the work as planning and carrying out measures to protect an organization’s computer networks and systems. See the [BLS computer and information technology occupations overview](https://www.bls.gov/ooh/computer-and-information-technology/home.htm).
Learn next: Networking (including TCP/IP, DNS, HTTP, and TLS), Linux and Windows administration, authentication and access control, cloud security, security monitoring, incident response, and vulnerability management. A portfolio project could analyze synthetic or public logs and explain how it detects a defined pattern, handles false positives, and protects sensitive data. Only test systems you own or have explicit permission to assess.
Good fit if: You enjoy investigation, systems, and thinking through how things can fail or be abused. Many security roles value prior IT, networking, or systems experience. A realistic route may start in technical support, system administration, network operations, or junior security work before progressing to engineering responsibilities.
4. Data engineer or database architect
Data engineers build the pipelines and platforms that move, validate, store, and prepare data for analysts, applications, and machine-learning teams. Python can extract data from APIs, transform records, orchestrate workflows, and automate data-quality checks. SQL and sound data modeling are just as central.
U.S. benchmark: BLS lists a $123,100 median annual wage for database administrators and architects in its computer-occupation comparisons. This is a nearby official category, not a direct measure of every data-engineering job; the title “data engineer” covers work that can differ substantially by employer.
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A strong portfolio project is a scheduled pipeline that ingests data, validates it, handles retries and failures, records useful logs, and loads a warehouse or database. Document how it responds to a schema change and how you would monitor its reliability. Knowing pandas is useful, but it does not by itself demonstrate production data-engineering skills.
Good fit if: You prefer databases, automation, and dependable infrastructure to designing user interfaces, and you like building systems other teams rely on.
5. Cloud or DevOps engineer
Cloud and DevOps engineers help deploy and operate software infrastructure. Python can automate cloud-resource management, deployment checks, monitoring, backups, and serverless tasks. The work also requires understanding the systems Python is automating: Linux, networking, cloud services, containers, and production operations.
Salary note: There is no single universal BLS salary figure for “DevOps engineer” or “cloud engineer.” Such jobs may be classified as software development, systems administration, network architecture, or another computer occupation depending on their actual duties. Treating one number as an official DevOps average would be misleading. O*NET’s [software-developer profile](https://www.onetonline.org/link/summary/15-1252.00) includes related titles such as DevOps engineer and infrastructure engineer, illustrating the overlap.
Learn next: Linux administration, networking, one cloud platform (AWS, Azure, or Google Cloud), Docker, CI/CD, infrastructure as code such as Terraform, identity and access management, observability, reliability, and incident response. A portfolio project could deploy a Python service using infrastructure as code and automated tests, with monitoring and a documented rollback plan.
Good fit if: You enjoy automation, troubleshooting, and keeping systems working in production. Cloud or DevOps skills can complement Python engineering and support progression into specialized infrastructure roles, but a Python course alone is not preparation for operating production systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which path should you choose?
| If you most enjoy… | Consider… | Skills to expect beyond Python |
|---|---|---|
| Building applications and APIs | Software or backend engineering | SQL, testing, Git, deployment, system design |
| Statistics, experiments, and predictive models | Data science or machine learning | Math, data analysis, model evaluation, deployment |
| Investigation and protecting systems | Cybersecurity | Networking, Linux, security operations, incident response |
| Databases, pipelines, and reliability | Data engineering | Advanced SQL, data modeling, orchestration, monitoring |
| Infrastructure, automation, and production troubleshooting | Cloud or DevOps | Linux, cloud, containers, CI/CD, infrastructure as code |
Do not choose solely by the biggest salary figure. The figures represent different occupations and experience mixes, and a path that matches your strengths may be more sustainable than one chosen for a headline number. Consider the entry barriers too: data science leans more heavily on statistics; security often rewards systems experience; cloud work requires infrastructure knowledge; software engineering demands sustained practice in building and maintaining software.
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What to learn after Python
- Use Git. Track changes, create a readable project history, and publish work in a well-organized repository.
- Learn SQL. Most paths involving applications or data benefit from knowing how to query and design databases.
- Get comfortable with the command line and Linux. These are especially important for backend, cloud, data, and security work.
- Write and test complete programs. Practice functions, modules, error handling, logging, debugging, and automated tests—not only short exercises.
- Learn APIs and one specialization. Choose a pathway, then build a project that reflects its real tasks.
- Deploy and explain your work. Include setup instructions, tests, design decisions, limitations, security considerations, and a clear account of what you would improve.
- Prepare for the hiring process. Practice technical interviews and communication, and look for internships, adjacent roles, or projects that provide relevant experience.
For several of these occupations, BLS identifies a bachelor’s degree as typical entry-level education. That is not a rule that every employer follows, but applicants without a degree may need to make their skills especially clear through relevant experience, substantial projects, or other evidence. Certifications can structure learning or signal familiarity with a platform; they do not substitute for being able to build, debug, and explain working systems.
Plan for the income you can actually expect
Use occupation-wide medians to understand the scale of a career, not to set a first-job budget. Before making a costly career switch, compare local wage data, review job postings for the roles and experience levels you can realistically target, and account for the time needed to build complementary skills. If you are weighing a course, certificate, IDE, or cloud subscription, choose it for a specific skill gap; paid tools are optional when free tools meet your needs. A portfolio that demonstrates sound decisions and reliable work is more persuasive than a collection of certificates alone.
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