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The right tech career depends on the work you want to do every day—not on a single “best” job. Compare the 20 paths below by deliverables, coding and math intensity, communication, entry routes, and ways to test your fit. A software developer may spend most of the day designing applications; a security analyst investigates suspicious activity; a UX designer studies how people use products. Each rewards a different combination of curiosity, technical depth, and working style.
The U.S. Bureau of Labor Statistics (BLS) projects 10% employment growth for software developers, QA analysts, and testers from 2025 through 2035. Across the broader computer-and-mathematical occupational group, BLS projects 10.1% growth from 2024 through 2034, adding about 267,700 jobs. Those figures describe specific occupational groups, not a guarantee for every role in this guide.
How to compare technology careers
Use the same questions for every option: What does the person produce? How much programming is involved? How much mathematics or statistics is routine? Who must they persuade or support? What is the normal entry route? What evidence can a beginner show? Finally, can you run a small experiment or interview someone in the role before committing?
O*NET’s Digital Technology Career Cluster groups related work in data and artificial intelligence, network systems and cybersecurity, IT support and services, software solutions, and web and cloud. Titles overlap: O*NET’s software-developer profile, for example, includes application developer, DevOps engineer, infrastructure engineer, software architect, software engineer, and systems engineer as reported job titles. Read job descriptions carefully rather than assuming a title has one universal meaning.
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| Path | Main output | Coding | Math/data | People interaction | Beginner evidence |
|---|---|---|---|---|---|
| Software developer | Applications and services | High | Low–medium | Medium | Deployed app and code history |
| QA/test engineer | Test plans, automation, defect reports | Low–high | Low–medium | Medium | Test strategy and automation suite |
| Data analyst | Reports, dashboards, decisions | Low–medium | Medium | High | Analysis with clear recommendations |
| Data scientist | Statistical models and experiments | Medium–high | High | Medium–high | Reproducible modeling project |
| Data engineer | Reliable data pipelines | High | Medium | Medium | Pipeline with tests and documentation |
| AI/ML engineer | Production machine-learning systems | High | High | Medium | Model served through an API |
| Cybersecurity analyst | Alerts, investigations, risk reports | Low–medium | Low–medium | Medium–high | Incident-lab write-up |
| Security engineer | Controls, secure architecture, automation | Medium–high | Low–medium | Medium | Hardened lab environment |
| Cloud engineer | Cloud infrastructure and services | Medium–high | Low | Medium | Infrastructure-as-code deployment |
| DevOps engineer | Delivery pipelines and reliability | Medium–high | Low | High | Automated build-and-release pipeline |
| Network engineer/administrator | Connected, available networks | Low–medium | Low | Medium | Network diagram and troubleshooting log |
| Systems analyst | Requirements and process improvements | Low–medium | Low–medium | High | Requirements case study |
| Database administrator | Secure, available databases | Medium | Low–medium | Medium | Backup, tuning, and recovery exercise |
| Web developer | Websites and web applications | Medium–high | Low | Medium | Accessible live site |
| Mobile developer | Phone and tablet applications | High | Low–medium | Medium | Published or installable app |
| UX/UI designer | Research, flows, interfaces | None–low | Low | High | Annotated design case study |
| Product manager | Priorities, road maps, outcomes | None–low | Low–medium | Very high | Product brief and experiment plan |
| IT support specialist | Resolved user and device issues | Low | Low | Very high | Knowledge-base articles and ticket simulations |
| Solutions architect | End-to-end technical designs | Medium | Low–medium | Very high | Architecture diagram with trade-offs |
| Technical writer | Usable technical documentation | None–low | Low | High | Published documentation portfolio |
The 20 career paths, explained
1. Software developer
Software developers design, build, test, and maintain applications or programs. The BLS describes the occupation plainly: “Software developers design computer applications or programs.” Daily work includes turning requirements into code, reviewing changes, debugging, and coordinating releases. Programming is central; mathematics is usually limited to logic and domain-specific calculations unless you work in graphics, scientific, or financial software. A computer-science degree is common, but a strong portfolio, internship, or apprenticeship can demonstrate readiness. Build a small application with automated tests, documentation, and a deployed version, then ask a developer how their team handles code review and production incidents.
2. QA or test engineer
QA engineers prevent defects by designing test plans, exploring edge cases, automating repeatable checks, and documenting failures clearly enough for developers to reproduce. Manual testing can require little code; automation and performance testing require substantial scripting and systems knowledge. Statistics is useful for risk-based test selection but is rarely the main discipline. Entry routes include a software background, a testing course, or support experience that developed investigative habits. Create a test strategy for a public web app, automate a focused regression suite, and report defects with environment, steps, expected result, and actual result.
3. Data analyst
Data analysts turn raw records into reports, dashboards, and recommendations. Expect substantial spreadsheet or SQL work, data cleaning, visualization, and conversations with stakeholders about what a metric really means. Programming ranges from light scripting to advanced SQL and Python; statistics is more important than in most application roles, though usually practical rather than theoretical. Business, economics, statistics, and self-directed projects can all lead in. Analyze an open dataset, publish the assumptions and limitations, and present three decisions a manager could make from the findings. An informational interview should focus on how analysts obtain trustworthy data and influence nontechnical audiences.
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Data scientists formulate questions, design experiments, build statistical or machine-learning models, and explain uncertainty. Coding is commonly medium to high, while probability, statistics, linear algebra, and experimental design are central. Employers often look for a quantitative degree or equivalent evidence through rigorous projects; advanced research roles may expect graduate study. Produce a reproducible notebook that compares baseline and improved models, uses a proper validation method, and discusses bias and error costs. Ask a practicing data scientist how often their work reaches production versus serving as analysis for a decision.
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5. Data engineer
Data engineers build the pipelines and storage systems that collect, transform, validate, and deliver data. Their output is dependable infrastructure: scheduled jobs, schemas, quality checks, and documentation. Programming is high, especially in SQL and a general-purpose language; mathematics is usually moderate and focused on data modeling and performance. Common entrances include software development, database work, or an engineering degree. Build a small pipeline that ingests source files, handles duplicate and late records, tests quality, and exposes a clean table for analysis. Interview someone about on-call duties and the consequences of a broken pipeline.
6. AI or machine-learning engineer
AI/ML engineers take models from experimentation into reliable products. They prepare data, train or fine-tune models, package inference, monitor drift, and manage latency and cost. Expect high programming and substantial statistics, linear algebra, and optimization. A degree in computer science, engineering, mathematics, or a related field helps, but a demonstrable end-to-end system can open interviews. Build a modest model, serve it behind an API, add tests and monitoring, and document where it fails. Ask an engineer how their team evaluates model quality, safety, and rollback procedures.
7. Cybersecurity analyst
Security analysts monitor alerts, investigate suspicious activity, assess vulnerabilities, and write reports that guide remediation. Scripting is useful but not always constant; analytical reasoning, evidence handling, and clear communication matter as much. Mathematics is light to moderate except in specialized cryptography or detection research. Entry routes include IT support, networking, security operations training, or a relevant degree and certification. Use an isolated lab to examine logs, construct a timeline, and write an incident report with containment and recovery steps. An informational interview can clarify shift schedules, escalation practices, and the difference between monitoring and threat hunting.
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Security engineers design and implement controls: identity policies, network protections, secure configurations, secrets management, and automated checks in development pipelines. Programming is medium to high when automation and infrastructure are involved; advanced mathematics is uncommon outside specialist areas. Most people arrive from systems, cloud, networking, or security-analysis roles, supported by hands-on labs and certifications. Harden a test environment, enforce least privilege, centralize logs, and record the trade-offs between security and usability. Ask an engineer which controls they own directly and how they measure whether defenses actually reduce risk.
Rank #3
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9. Cloud engineer
Cloud engineers provision and operate compute, storage, networking, identity, and managed services. Their work output is repeatable infrastructure, observability, cost controls, and recovery plans rather than a single application feature. Coding is medium to high through scripting and infrastructure-as-code; mathematics is generally low. A systems or developer background, cloud training, and a documented lab are common entry routes. Deploy a small service from code, restrict permissions, add monitoring, and test a backup or failover. Ask about the team’s on-call rotation and how it prevents configuration drift.
10. DevOps engineer
DevOps engineers improve the path from code change to dependable operation. They create build and release pipelines, automate environments, instrument services, and help teams respond to incidents. Programming is medium to high, especially scripting; math is low, while communication and negotiation are high because the role connects development and operations. Many enter from software development, system administration, or QA. Build a pipeline that tests a sample service, creates an artifact, deploys to a staging environment, and supports a safe rollback. Interview a DevOps practitioner about reliability targets and how after-hours incidents are handled.
11. Network engineer or administrator
Network professionals keep wired, wireless, and virtual networks connected, secure, and observable. They configure devices, segment traffic, troubleshoot latency, manage addresses, and document changes. Programming is usually low to medium, with automation increasingly valuable; mathematics is light. Networking courses, a certification, help-desk experience, or a lab can provide an entry route. Design a small network, capture and interpret traffic in a legal lab, and write a troubleshooting decision tree. Ask how the organization manages maintenance windows and validates changes before they affect users.
12. Systems analyst
Systems analysts study how an organization works, identify technology gaps, translate needs into requirements, and recommend process or system changes. Coding may be occasional; interviewing, process mapping, data interpretation, and negotiation dominate. A business, information-systems, or domain degree is useful, but operational experience can be equally persuasive. Create a case study that maps a flawed workflow, defines measurable requirements, compares two solutions, and explains implementation risk. Speak with an analyst about handling conflicting stakeholder priorities and verifying that a delivered system solved the original problem.
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13. Database administrator
Database administrators maintain availability, performance, security, backups, and recovery for data stores. SQL and scripting are important; mathematics is modest, with more emphasis on data structures, transactions, and capacity planning. People often enter from database development, systems administration, or data engineering. In a disposable environment, design a schema, implement access controls, measure a slow query, and perform a restore test; document every assumption. Ask a DBA how they plan maintenance, test recovery objectives, and respond when a production change goes wrong.
14. Web developer
Web developers create and maintain browser-based sites and applications. Front-end work emphasizes semantic HTML, CSS, accessibility, and JavaScript; back-end work adds APIs, databases, and security. Mathematics is low for most projects, while collaboration with designers, writers, and clients is routine. A portfolio and practical experience can substitute for a formal degree in many entry-level searches. Build an accessible, responsive site with fast loading, form validation, and a small content-management or API integration. Ask a developer how they test across browsers and handle accessibility defects.
15. Mobile developer
Mobile developers build applications for phones and tablets, managing platform conventions, offline behavior, permissions, notifications, and release processes. Programming is high; mathematics is usually low to moderate unless the app uses graphics, sensors, or specialized algorithms. A computer-science program, internship, or portfolio app is a common route. Create a focused app that stores data safely, handles network loss, includes automated tests, and can be installed by another person. Interview a mobile developer about review guidelines, crash monitoring, and supporting multiple operating-system versions.
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UX/UI designers research user needs, organize information, sketch flows, prototype interactions, and refine visual interfaces. Coding is optional to low, although understanding front-end constraints helps; mathematics is minimal. Communication is high because designers explain decisions, run usability sessions, and incorporate feedback. Entry evidence is a portfolio of case studies, not a gallery of isolated screens: show the problem, research, iterations, and outcome. Redesign a confusing task, test the prototype with several representative users, and record what changed. Ask a designer how research findings influence product priorities.
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17. Product manager
Product managers decide which problems to solve, align stakeholders, set priorities, and measure outcomes. They may read and discuss code but rarely write production software. Quantitative ability helps with metrics and experiments; communication, judgment, and conflict management are the largest parts of the job. People commonly move from engineering, design, analysis, sales, or domain expertise. Write a product brief with a target user, success metric, constraints, prioritized options, and experiment plan. Interview a product manager about saying no, handling incomplete evidence, and sharing accountability with engineering and design.
18. IT support specialist
IT support specialists resolve device, account, software, and connectivity problems while protecting users and documenting repeatable fixes. Coding is low, though scripting can accelerate routine work; patience, diagnosis, and communication are essential. Entry routes include a support course, practical lab, customer-service experience, or an entry-level certification. Simulate a ticket queue, create clear knowledge-base articles, practice secure identity verification, and record escalation criteria. Ask a support specialist how they balance speed, user education, privacy, and service-level commitments.
19. Solutions architect
Solutions architects turn business goals into end-to-end technical designs spanning applications, data, integration, security, and operations. They need broad technical literacy rather than mastery of every implementation detail. Coding is medium and math is low to moderate, while workshops, presentations, and trade-off discussions make communication very high. Most enter after substantial experience in development, infrastructure, or consulting. Draw an architecture for a realistic workload, include failure modes, costs as stated assumptions, security boundaries, and migration steps. Ask an architect how they prevent attractive diagrams from becoming unbuildable plans.
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Technical writers produce documentation that enables people to install, use, troubleshoot, or govern a technical product. Coding is none to low; understanding systems, information design, editing, and interviewing subject-matter experts are central. A writing, communications, or technical background can lead in. Build a short documentation set with a task guide, reference page, troubleshooting section, and change log; test it with someone unfamiliar with the product. Interview a technical writer about review workflows, version control, and measuring whether readers succeed without opening a support ticket.
A practical way to choose
- Describe your preferred evidence. Do you enjoy shipping working software, finding anomalies, explaining systems, designing experiences, or helping people solve immediate problems? Write three tasks that energize you and three that drain you.
- Choose two adjacent paths. Pair alternatives that share skills but differ in emphasis: data analyst versus data scientist, security analyst versus security engineer, cloud versus DevOps, UX/UI versus product management, or web versus mobile development.
- Run a two-week trial for each. Produce a small, finished artifact using the examples above. Track time spent, frustration, curiosity, and whether you wanted to improve the result after it worked.
- Collect an honest job sample. Read several current postings in your region and level. Note recurring tools, education requirements, on-call expectations, portfolio demands, and collaboration patterns. Titles alone are not reliable.
- Conduct an informational interview. Ask what the person delivered last week, what surprised them after entering, which tasks consume most of their time, and what evidence would make a beginner credible. Do not ask only whether they “like” the career.
- Close the evidence gap. Select one structured course, certification study guide, cloud training program, cybersecurity learning resource, or career assessment tool only after checking its syllabus, cost, currency, and recognition in the jobs you want. Pair study with a demonstrable project.
- Reassess after feedback. Have a practitioner review your artifact against a real job description. Choose the path whose daily work and learning process fit you—not merely the title with the most impressive forecast.
What the outlook numbers do—and do not—tell you
BLS is useful because its occupational profiles apply consistent definitions to duties, entry-level education, median pay, and outlook. The 10% projection for software developers, QA analysts, and testers covers 2025–2035. The 10.1% projection and 267,700 additional jobs apply to the entire computer-and-mathematical group over 2024–2034. Neither statistic ranks the 20 paths, predicts your local hiring market, or replaces reading role-specific profiles. Treat outlook as one input alongside your strengths, preferred work setting, preparation time, and access to entry-level opportunities.
Do you need to code for a technology career?
No. Software, data engineering, AI/ML, mobile development, and much of cloud and DevOps require regular programming. QA automation, security engineering, database administration, and web development range from moderate to high coding. UX/UI design, product management, systems analysis, IT support, and technical writing can be successful with little or no production code. Even non-coding roles benefit from technical literacy: it helps you ask precise questions, spot constraints, and collaborate without promising what a system cannot do.
What can you do with computer-science skills?
You can specialize in building products, operating infrastructure, protecting systems, analyzing information, designing experiences, coordinating decisions, or explaining complex tools. A computer-science foundation keeps several doors open because programming, data structures, systems thinking, and debugging transfer across paths. Your first job does not permanently lock your career; moving from support to security, development to solutions architecture, or analysis to data science is common when you deliberately build the missing evidence.
Quick Recap
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