What Does a Test Automation Engineer Do? STLC Phases and Career Path

A test automation engineer writes code that checks whether software works, then runs those checks automatically on every release. I have managed web projects since 2012, and this role is the one that most often stops a site from breaking right before launch. In this guide I explain the role, the STLC phases and the career path.

I am not comparing tools here. Selenium, Cypress and Playwright deserve their own article. The focus in this piece is the job itself: what you do, in which order you do it, and how you grow in this field. You can find my other engineering articles in the software category.

What is a test automation engineer and what do they do?

A test automation engineer is a software quality specialist who turns repetitive manual test steps into code, connects those tests to the continuous integration pipeline and reports results clearly to the team. The core goal is to prove within minutes that every code change leaves the application working.

In practice the role combines three things. First, you need test design knowledge. Writing code without knowing what to test is pointless. Second, you need software development skills, because test code is real code that needs maintenance. Finally, communication matters. If you cannot explain a bug clearly, the fix gets delayed.

In the projects I run, this person usually sits inside the product team. As a result, they join requirement meetings, spot risks early and build tests at the same pace as development. In short, the role is not "the person who checks at the end". It is the person who designs quality from the start.

How is a test automation engineer different from a manual tester?

Both roles ask the same question: does the software behave as expected? However, they reach the answer in different ways. A manual tester explores the application like a real user and relies on intuition to catch surprising behavior. An automation engineer makes repeatable checks permanent.

This table does not say one role is unnecessary. On the other hand, a good team leaves exploration to people and repetition to machines. I know many people who moved from manual testing into automation. Their test design background became their biggest advantage in that move.

What does a typical day look like for a test automation engineer?

The day usually starts with the results of the nightly test run. You look at the red tests and decide whether each one shows a real bug or a flaky test. In practice, that decision is the most valuable part of the job. False alarms burn through team trust very quickly.

A typical day includes these tasks:

• Reviewing the nightly run and triaging failed tests.
• Discussing test risks of new work in the daily stand-up.
• Writing test cases for new features and turning them into code.
• Reviewing pull requests from a testing point of view.
• Fixing flaky tests and keeping test data and environments current.

Also, part of every day goes to maintenance. The interface changes, a field gets renamed and twenty tests break at once. For this reason, a well designed test architecture saves even more time than writing new tests does.

What is the STLC and how does it relate to the SDLC?

The STLC, or software testing life cycle, is a framework that orders testing activities from planning to closure. The SDLC describes the whole software development process. Put simply, you can think of the STLC as a quality focused sub process that runs inside the SDLC.

Sources name the phases differently. For example, the ISTQB Foundation Level syllabus describes the test process as planning, monitoring and control, analysis, design, implementation, execution and completion. The simpler model most teams use has six steps. They are requirement analysis, test planning, test case development, environment setup, test execution and test closure.

So I use the six step model in this article because teams understand it quickly. Still, keep one ISTQB point in mind. These activities do not have to follow a strict sequence. In agile teams, the same cycle repeats on a small scale in every sprint.

What happens in requirement analysis, the first STLC phase?

In requirement analysis you decide what is testable. You read the user story, flag vague wording and sharpen the acceptance criteria. For example, a sentence like "the form should be fast" cannot be tested. By contrast, "form submission should respond in under two seconds" can.

An automation engineer asks one more question at this stage. Which part of this requirement can I automate? For example, if card validation in a checkout flow depends on a third party service, you may need a mock instead of the real service. Spotting this during analysis makes planning far more realistic.

After that, the usual output is a requirements traceability matrix. The matrix links each requirement to its tests. Therefore, before a release you can see at a glance which requirement lacks coverage. On web projects I also add conversion steps to this matrix. Setting conversion goals early also shows which flows are truly critical.

Which decisions do you make during test planning?

A test plan brings scope, approach, resources and schedule into one document. At this stage you write down what you will not test as well as what you will. Naming out of scope areas openly prevents the "why did nobody test this" argument after release.

The key planning decisions are these:

1. Which test levels will run: unit, integration or end to end.
2. Which scenarios become automated and which stay manual.
3. Which browsers, devices and environments the tests will cover.
4. What the entry and exit criteria are.
5. What the risks are and what the fallback plan is for each.

From an automation angle, the most important decision is return on investment. Automating a test costs more than running it by hand a few times. Consequently, you prioritize scenarios that repeat often and live long. Building heavy automation for a one off campaign page rarely makes sense.

How do you prepare test cases and test data?

In this phase you translate requirements into step by step test cases. Each case has a precondition, steps, an expected result and defined data. Then you turn the suitable cases into code. Above all, a good test checks one thing. Squeezing five checks into one test makes debugging painful.

Test design techniques come in here. Equivalence partitioning, boundary value analysis and decision tables reduce endless possibilities to a manageable number of tests. For instance, if a field accepts ages from 18 to 65, testing 17, 18, 65 and 66 finds more bugs than hundreds of random values.

Meanwhile, test data is the part people underestimate. Using real customer data in a test environment carries legal and ethical risk. That is why you generate synthetic data or apply masking. In addition, when every test creates and cleans up its own data, tests stop affecting each other.

Why is test environment setup a separate phase?

Because even the best test gives the wrong answer in the wrong environment. A test environment combines servers, databases, third party services, browsers and configuration. The closer it is to production, the more you can trust the result.

In practice, containers make this phase much easier today. You describe the environment in code and spin up the same setup from scratch for every run. As a result, the "it worked on my machine" problem mostly disappears. In projects built on microservices or micro frontend architecture, environment management matters even more, since the number of moving parts grows.

Next, once the environment is ready, you run a short smoke test. It checks whether the application starts and whether core pages respond. If the smoke test fails, running the full suite is pointless. You fix the environment first.

How do test execution and bug reporting work?

During execution you run the prepared tests, record results and investigate failures. In automation this step is usually tied to the CI pipeline. When a developer pushes code, the tests start on their own and results arrive within minutes.

When a test fails, your first job is to reproduce the problem. Then you write a good bug report. A report I trust contains the following:

• A short, descriptive title.
• Steps that reproduce the bug.
• Expected result and actual result.
• Environment, browser and version details.
• A screenshot, video or log excerpt.
• Severity and business impact.

Above all, a big advantage of automation is that it collects most of this evidence by itself. Modern tools store screenshots, network requests and a step by step trace of failed tests. So a developer can understand the bug without booking a meeting with you.

What do you evaluate during test closure?

Closure is more than declaring that testing is done. You check whether exit criteria are met, list open bugs and feed data into the release decision. That said, the decision belongs to the product team. Your job is to present risk clearly and measurably.

A closure report usually covers the number of executed tests, the pass rate, the severity spread of bugs and areas left out of scope. You also write down lessons learned. Which bug was found late and why? Also, which test never caught anything? Which area caused more trouble than expected?

After that, these lessons flow into the next cycle's plan. In other words, the STLC works like a loop. I recommend the same habit on the marketing side. A team that knows how to read a report makes the next decision with less guesswork.

Which STLC phases does automation touch?

Many people link automation only with execution. Yet a test automation engineer produces work in every phase of the cycle. In analysis they judge automation fit. Next, in planning they choose the approach and framework. In design they turn tests into code.

During environment setup they define infrastructure as code. During execution they wire tests into the pipeline. At closure they generate reports automatically. In the most mature teams, test results even act as a quality gate. Code cannot reach production until critical tests pass.

For that reason, job ads under the title "test automation engineer" actually ask for several skills. They want test design, software development, infrastructure knowledge and reporting. The scope varies from company to company. The common thread is making quality repeatable and measurable.

Which tests should you automate and which should you skip?

Automating every test is neither possible nor sensible. Instead, I use a simple rule. A test is a candidate for automation when it repeats often, its result can be verified objectively and the feature will live for a long time. If one of these conditions is missing, think twice.

Typical areas that suit automation are:

• Regression tests that confirm the same flows still work on every release.
• Critical business flows such as login, sign up, cart and checkout.
• API tests, which are fast, stable and easy to maintain.
• Data driven tests that run one flow with hundreds of inputs.
• Basic checks across browsers and screen sizes.

On the other hand, exploratory testing, usability reviews and whether a design "feels right" need human eyes. Automation written for an unstable, fast changing interface breaks every week. Its maintenance cost then outweighs the gain. For example, during a mobile friendly test you can automate the basic checks and leave readability judgment to a person.

How does the test pyramid shape your automation strategy?

The test pyramid is a simple model for balancing tests by level. At the base you find many fast unit tests. The middle holds fewer integration and API tests. Finally, the top holds a small number of end to end UI tests. The logic is simple: lower tests are fast and cheap, while higher tests are slow and fragile.

The most common mistake I see is an upside down pyramid. In that setup, the team tries to test everything through the browser. The suite takes an hour and half the tests fail at random. Eventually nobody trusts the results and the automation investment goes to waste.

As an automation engineer, your job is to push each check to the lowest possible level. You verify a discount calculation with an API or unit test, not through the UI. You keep UI tests to prove the user's end to end journey. This keeps the suite fast and reliable. Moreover, when a low level test fails, it points to a much narrower area. The developer can go straight to the right function.

How do you deal with flaky tests?

A flaky test passes sometimes and fails sometimes while the code stays the same. These tests are the quietest enemy of automation. After a few false alarms, the team starts to ignore red results. Real bugs then disappear in the noise.

Specifically, the usual causes are well known. Fixed sleep timers, clicking before a page has loaded, shared data between tests and dependence on external services top the list. Therefore you replace fixed waits with condition based waits. You also make every test create its own data.

My practical advice is simple. When you spot a flaky test, move it out of the main suite, open a ticket and fix it within a set time. Keeping it green forever with automatic retries hides the problem instead of solving it. In short, ten reliable tests are worth more than a hundred doubtful ones.

How should you structure a test automation framework?

A framework is the skeleton in which you write, run and report tests. However, picking a tool is only one part of it. The real goal is tests that are readable, reusable and easy to maintain. In a well built framework, adding a new test takes minutes.

A solid framework usually has these layers: page or component objects, test data management, environment configuration, shared helpers and reporting. For example, if the selectors of a login page live in one file, a UI change means updating that one file instead of twenty tests.

You also need to keep the framework clear for the rest of the team. If developers can write tests, quality does not rest on one person's shoulders. For that reason you write a short guide, add sample tests and review test code as seriously as application code.

Which metrics show whether automation is working?

First, test count alone is a poor metric. A suite of thousands of tests that never catches a bug is not better than a few dozen tests that catch critical ones. So I suggest looking at impact rather than volume.

Meaningful metrics include total suite runtime, flaky test rate, bugs that escape to production and automation coverage of critical flows. For instance, if the suite grows from twenty minutes to an hour, developers stop waiting for feedback. Speed is therefore a quality metric too.

Reporting these metrics regularly makes the value of automation visible. On the marketing side, choosing the right KPIs is the basis of good decisions. The same is true for testing: the right metric sets the direction of the investment.

What technical skills does a test automation engineer need?

A technical foundation is the first investment for anyone who wants to become a test automation engineer. At the top of the list is at least one programming language. Java, Python, JavaScript and TypeScript are the most common choices. I suggest picking the language your team uses for the product, because shared tooling makes collaboration easier.

Next, these skills follow:

1. HTML, CSS selectors and a basic grasp of how browsers work.
2. HTTP, REST and JSON, which you need for API testing.
3. Version control with Git and a habit of code review.
4. Continuous integration tools and the command line.
5. Basic SQL, which you will use often to verify test data.
6. Design patterns such as Page Object and clean code principles.

Basic knowledge of performance and accessibility testing also helps you stand out. For example, adding checks like a Lighthouse performance test to the pipeline catches speed regressions before launch.

Which soft skills make a difference in this role?

Technical skills get you hired; however, soft skills do more. Soft skills make you the person the team trusts. Curiosity comes first, because it drives every good test. A good test engineer asks "what happens if this field stays empty" before anyone else does.

The second skill is kind but clear communication. Reporting a bug means pointing out a gap in someone's work. So you use neutral, evidence based language. Saying "I get this result with these steps" instead of "your code is broken" protects the relationship and speeds up the fix.

The third skill is prioritization. If you report every bug with the same urgency, the team cannot truly prioritize any of them. You weigh business impact and decide which bug should block a release and which can wait. In short, business understanding is the least discussed and most valuable trait of a test engineer.

How does the test automation career path progress?

Titles differ between companies, but the general path looks similar. Many people start as manual testers or software developers. Some join directly as junior automation engineers. The levels below are a general framework based on field experience. They are not identical in every company.

1. Junior: writes tests inside an existing framework and triages failures.
2. Mid level: builds test strategy for new modules and improves the framework.
3. Senior: designs test architecture, reviews code and mentors juniors.
4. Lead or QA manager: runs the quality strategy of several teams.
5. SDET, DevOps or platform engineering: moves toward infrastructure.

In practice, the time between these steps depends on the person. What I have seen is that scope matters more than years. Someone who builds a framework from scratch grows faster than someone who only updates existing tests for years.

What do certifications and labor market data say about this job?

Certification is not mandatory, but it gives you a shared vocabulary early in your career. ISTQB Foundation Level is a widely recognized starting point for test terminology and process. ISTQB also offers an advanced certification focused on test automation engineering. Even so, hiring managers usually look harder at real projects on your GitHub.

For an official reference on the market, the US Bureau of Labor Statistics reports a median annual wage of $104,300 for software quality assurance analysts and testers as of May 2025. The same source projects 6 percent employment growth for this occupation from 2025 to 2035.

Still, these figures describe the US market. Salaries elsewhere differ a lot. However, they show that the role is a lasting global need. Remote work also lets engineers in other countries join international projects.

How is AI changing the role?

AI coding assistants clearly speed up writing test scaffolding. Pointing at a function and asking for boundary value tests now takes seconds. However, checking whether those drafts are correct is still your responsibility.

In practice, my observation is that assistants help most with repetitive, pattern based work. Updating selectors, generating data and summarizing reports lead the list. On the other hand, deciding what to test, which risk matters to the business and which test is redundant still needs human judgment.

So AI does not remove the role. It shifts its center of gravity. Coding time shrinks, while test strategy and result interpretation become more valuable. That is why investing in test design fundamentals early in your career looks like the longest lasting bet. If you want to see how AI is changing the web side as well, read my piece on technical SEO after AI.

What do web projects gain from test automation?

For example, on the web side I see the gain most clearly on release days. A broken form field, a failed redirect or a blocked checkout step means ad budget quietly going to waste. Automated tests catch these breaks before visitors do.

For example, during a site migration, checking old URLs by hand can take days. A test script verifies hundreds of redirects in minutes. You can use the redirect checker for single checks and the website migration checklist for the full process.

In my web design projects, automated checks of critical flows are a standard step before launch. That way I know the sales flow still works when the design changes. The mindset of a test automation engineer gives marketing teams the same confidence.

How should you plan your first 90 days in this career?

If you start from zero, spend the first 30 days on the basics of one programming language and core testing concepts. Specifically, variables, loops, functions and classes are enough. In the same period, learn test levels, test design techniques and the STLC phases.

In the second 30 days, pick a small demo application and write a few end to end tests for it. Then add tests for its API. Do not fear mistakes here, since every broken test teaches you something. Put the tests in a Git repository and connect them to a CI pipeline that runs on every push.

In the last 30 days, turn the project into a real portfolio. Set up a Page Object structure, add reporting and explain your approach in a README file. At that point you talk about a working system in interviews, not theory. That concrete proof is what lands your first job as a test automation engineer. If you need help with the quality process of a web project, you can reach me through the contact page.