A quality assurance engineer finds software risks before they reach users. They review requirements, design tests, automate repeatable checks, test APIs and integrations, investigate failures, and help engineering teams decide whether a release is ready.
The most useful technical principle is simple: test each risk at the lowest reliable layer. Put calculation rules close to the code, service behaviour at the API, integration risks between components, and only critical user journeys in end-to-end tests. This gives developers faster feedback and makes failed tests easier to diagnose.
What does a quality assurance engineer do day to day?
A QA engineer turns product and technical risks into checks a team can repeat.
Typical responsibilities include:
- reviewing requirements and acceptance criteria
- identifying edge cases before development starts
- planning manual and automated testing
- testing APIs, databases, integrations, and interfaces
- writing and maintaining automated tests
- running exploratory and regression tests
- reproducing and documenting defects
- investigating failed tests
- integrating quality checks with CI/CD
- assessing release risk with developers and product teams
The balance depends on the product.
A content platform may put more weight on browser behaviour, accessibility, and usability. A FinTech product has different failure modes. Transaction integrity, retries, rounding, authentication, permissions, third-party APIs, and audit records can all become release-critical.
This is why QA works best as an engineering discipline rather than a final testing phase. Code & Pepper’s Quality Assurance services cover automated, functional, compatibility, stress, UI, and unit testing.
Quality starts before the first test runs
Useful QA often begins while a requirement is still being written.
Consider a wallet feature with one requirement:
A user can withdraw their available balance to a bank account.
The first QA question should be: what does “available” mean?
Can a user withdraw money tied to a pending transaction? What happens when two withdrawal requests arrive almost together? Can the client retry after a timeout? What happens if the bank processes the transfer but the application never receives confirmation?
Those questions expose missing product and architecture decisions before code exists.
Finding the gap during refinement gives developers a clear rule to implement. Finding it after development may mean changing the API, database, interface, and existing tests.
This is the practical value of shift-left testing. Code & Pepper’s guide to continuous testing in DevOps explains how tests can run throughout the delivery pipeline instead of waiting for a separate testing phase.
Put each test at the right layer
A good test strategy matches each risk with the cheapest reliable way to detect it.
| Test layer | Best use | Example |
| Unit | Isolated business logic | Fee calculation |
| Integration | Components working together | API writes a transaction to a database |
| API | Service behaviour | Withdrawal endpoint rejects an invalid amount |
| Contract | Service compatibility | Provider changes its response schema |
| UI | Interface behaviour | Form validation |
| End-to-end | Critical journeys | Login, payment, confirmation |
| Performance | Load and degradation | API latency under peak traffic |
| Exploratory | Unknown risks | Unexpected user behaviour |
The tradeoff is speed versus scope.
End-to-end tests exercise more of the system, but they also depend on more components. A failed browser test might come from application code, authentication, test data, the network, an external provider, or the test itself.
A fee calculation does not need a browser to prove its arithmetic is correct. Keep that check near the business logic.
Use end-to-end tests when the interaction between components is part of the risk. This gives teams broad coverage where it matters without making every build depend on a large browser suite.
Manual testing and automation solve different problems
Automation works best for stable behaviour with a clear expected result.
API contracts, calculation rules, permissions, smoke tests, regression checks, and critical user flows are strong candidates.
Exploratory testing handles uncertainty.
A tester exploring a new onboarding flow may find confusing behaviour, inconsistent state, or a sequence nobody included in the specification. Automating every scenario too early can create maintenance work without adding much confidence.
A practical rule is:
Automate a check when it is repeatable, deterministic, valuable, and likely to run often.
Keep human exploration for new, uncertain, or fast-changing behaviour.
The goal is reliable feedback, not the highest automation percentage.
QA automation is an architecture decision
Modern test automation usually spans several layers.
A web product might use unit tests for business logic, API tests for services, Playwright or Cypress for critical browser flows, and k6 or Artillery for performance checks. CI/CD controls when each suite runs.
Tool choice should follow the application architecture.
If most business logic sits behind an API, hundreds of UI tests may create more maintenance than confidence. API-level checks can cover many rules faster and isolate failures more clearly.
Microservices create another problem. A service can change its API in a way that breaks a consumer even when both applications pass their own unit tests. Contract tests can detect that mismatch without starting the whole platform.
Code & Pepper’s test automation architecture guide covers framework design, API and UI testing, CI/CD integration, flaky tests, and maintaining automation as a product changes.
A $500 payment retry shows what good QA actually tests
Consider a withdrawal endpoint:
POST /withdrawals
A provider processes a $500 withdrawal. Before it returns confirmation, the connection times out.
The application does not know whether the operation failed. The client retries.
A weak implementation may create a second $500 withdrawal.
A useful QA strategy tests the failure state:
- Send the same logical request twice.
- Verify the idempotency rule.
- Simulate a provider timeout.
- Return a delayed provider response.
- Check the final database state.
- Verify the status shown to the user.
- Confirm that only one financial operation exists.
- Check whether logs can reconstruct the event.
The important question is not simply, “Does withdrawal work?”
It is: Does withdrawal remain correct when the network fails at the worst possible point?
Code & Pepper encountered a related retry requirement while working with DirectID. The DirectID case study describes a three-layer architecture using Azure SQL Database and Microsoft Azure. The team used xUnit, FakeItEasy, and Polly for unit and integration testing, including retry behaviour.
This is where QA becomes part of architecture rather than a checklist applied after development.
Third-party APIs need controlled failure states
External APIs are difficult to test when their sandboxes cannot reproduce realistic failures.
The problem appears with payment gateways, open banking providers, identity services, email platforms, healthcare integrations, and other external systems.
Teams may need mocks, stubs, test doubles, or controlled environments to reproduce:
- timeouts
- duplicate callbacks
- delayed responses
- malformed payloads
- rate limits
- expired credentials
- partial processing
The important property is control.
If a product depends on an external service for a critical workflow, engineers should be able to reproduce important failure states without waiting for the provider to fail in production.
This matters in financial software because a timeout does not prove a transaction failed. The external operation may already have completed.
CI/CD needs fast quality gates
Automated QA works best when each pipeline stage runs checks that provide useful feedback at that point.
A practical structure looks like this:
Pull request: unit tests, static analysis, and selected integration tests.
Build: broader API and integration checks.
Staging: smoke tests and critical end-to-end journeys.
High-risk release: targeted performance, migration, security, or regression checks.
Production: health checks and synthetic monitoring where appropriate.
A huge browser suite on every commit can slow developers down. Running only unit tests can leave integration failures invisible.
The better approach uses progressively broader gates. Fast checks should fail fast. Slower checks should run when their wider scope justifies the extra execution time.
Flaky tests can damage the delivery pipeline
A flaky test fails even though the application behaviour it checks has not changed.
Once this happens often enough, developers stop trusting the pipeline. Instead of investigating a red build, they rerun it.
Common causes include timing assumptions, unstable selectors, shared state, poor test data, external dependencies, and badly handled asynchronous operations.
Retries can help identify a temporary infrastructure issue. They should not hide an unreliable test.
Repair or quarantine flaky checks. A smaller suite that engineers trust is more useful than a larger suite they routinely ignore.
Regulated software changes the QA risk model
FinTech and HealthTech products add engineering constraints around access, sensitive data, traceability, transaction integrity, and failure recovery.
Depending on the product and applicable requirements, QA may need to verify:
- authentication and permissions
- role boundaries
- transaction integrity
- audit records
- sensitive-data handling
- recovery after failure
- security controls
- third-party integration behaviour
QA should not independently declare that a product complies with FCA rules, PSD2, GDPR, HIPAA, or another regulatory framework.
The engineering task is narrower and more useful. Applicable product, security, and regulatory requirements should become testable conditions and repeatable evidence.
Security verification also extends beyond functional tests.
NIST IR 8397 recommends software verification techniques that include threat modelling, automated testing, static code scanning, checks for hardcoded secrets, black-box and structural testing, fuzzing, and checks on included libraries and services.
For engineering leaders, the lesson is practical. Security verification belongs inside software delivery rather than one late testing event.
Test coverage is evidence, not proof of quality
Coverage tells you which code ran during testing. It does not prove that the right assertions were made.
A system can have high coverage and still miss race conditions, broken contracts, incorrect permissions, or realistic failure states.
Code & Pepper’s Dinarak mobile payments case study reports 90% test coverage for the application. The project included rebuilding the front end in React Native for a digital wallet and payments product.
The Smart Pension case study gives another useful example. Its delivery process merged new branches more than 20 times a day, while the code had nearly 100% test coverage.
Those percentages matter because they sit inside a delivery context.
For a CTO, the more useful question is:
Which expensive failures can still escape the suite?
QA engineer, tester, SDET, or automation engineer?
Job titles overlap. Scope matters more.
| Role | Typical focus |
| Software tester | Manual execution, exploration, defect reporting |
| QA analyst | Requirements, test design, process quality |
| QA engineer | Technical testing, APIs, risk, automation, CI/CD |
| QA automation engineer | Frameworks, coded tests, CI integration |
| SDET | Software engineering focused on testability and automation |
| Automation architect | Automation strategy across teams and systems |
For a technical buyer, ownership is the useful distinction.
A senior QA hire should be able to discuss system boundaries, API behaviour, test data, failure states, CI/CD, and testability. Knowing the syntax of one automation framework is not enough.
Code & Pepper’s August 2026 QA engineer interview guide focuses on strategy, automation, APIs, performance, security, and technical reasoning.
The skills that matter most
A strong QA hire needs enough technical depth to investigate a system instead of treating it as a black box.
For many software teams, useful skills include:
- HTTP and REST APIs
- SQL and data validation
- a programming language suited to the stack
- test automation
- Git and CI/CD
- logs and basic observability
- authentication and permissions
- risk-based test design
A QA specialist does not need to be a full-stack developer.
They do need enough engineering skill to write maintainable tests, inspect API traffic, query data, debug failures, and discuss defects with developers.
Risk judgement matters just as much.
A visual defect on an internal admin page and an incorrect payment calculation are both bugs. They should not receive the same release priority.
How long does QA automation take?
There is no reliable universal timetable for implementing test automation.
The schedule depends on the existing test suite, application architecture, environments, test data, CI/CD maturity, browser or device coverage, external integrations, and technical debt.
A useful implementation sequence is:
| Phase | Main outcome |
| Audit and risk mapping | Critical journeys and current gaps identified |
| Framework design | Test layers, tools, data strategy, and ownership defined |
| First automation | High-value repeatable checks running reliably |
| CI/CD integration | Relevant checks become delivery gates |
| Expansion | Coverage grows according to risk |
| Maintenance | Flaky, obsolete, and low-value tests are removed |
A small proof of concept is different from a production test system.
A team automating several stable API flows may produce useful checks quickly. A team inheriting a legacy product with unstable environments, shared test data, several external providers, and a large browser regression suite has a different job.
A CTO should therefore ask for scope, dependencies, milestones, and exit criteria, not a generic promise that automation takes a fixed number of weeks.
What does a QA engineer cost in the US?
Current US government data provides a useful baseline for permanent employment.
The US Bureau of Labor Statistics reports a $104,300 median annual wage for software quality assurance analysts and testers in May 2025. The lowest 10% earned less than $61,440, while the highest 10% earned more than $167,010.
BLS occupational wage data also reports a $111,490 mean annual wage and $53.60 mean hourly wage for the occupation in May 2025.
These are labour-market benchmarks. They are not Code & Pepper rates and should not be treated as the full cost of employing or contracting an engineer.
Benefits, recruitment, equipment, management time, seniority, location, contract structure, and supplier costs can all change the commercial comparison.
Capability changes it too.
A manual tester, API-focused QA engineer, senior SDET, and automation architect solve different problems. Comparing them only by hourly cost can hide large differences in technical ownership.
Code & Pepper does not publish a universal QA rate card on its QA service page. A fixed Code & Pepper price here would therefore create false precision.
When does a team need dedicated QA?
Team size is a poor trigger. Release risk is better.
Dedicated QA becomes more valuable when regressions reach production, developers spend significant time repeating manual checks, external integrations multiply, or nobody owns cross-system failure testing.
It matters even more when software handles money or sensitive data.
The business case should compare QA cost with the work and risk it can reduce:
- developer time spent on manual regression
- delayed releases
- production incident investigation
- emergency fixes
- support workload
- recurring defects
Developers still own quality.
QA adds specialist knowledge of test architecture, system risk, failure analysis, and release evidence. It should strengthen engineering ownership rather than move responsibility away from developers.
Hire for reasoning before tool knowledge
A technical QA interview should give the candidate a system problem.
For example:
A payment provider sometimes times out after processing a transaction. How would you test the retry behaviour?
A strong candidate should ask questions before naming a framework.
They may discuss idempotency, provider simulation, API state, database assertions, logs, race conditions, test data, and regression coverage.
That tells you more than asking for a definition of regression testing.
For a senior role, also test how the candidate handles API versus UI automation, asynchronous workflows, flaky tests, permissions, contract testing, migrations, and performance risks.
The goal is to find someone who can choose what to test, at which layer, and explain why.
AI changes the test surface, not the core QA problem
AI can assist with test-case generation, synthetic data, automation code, and first-pass failure analysis. Engineers still need to judge whether those outputs are correct and useful.
AI-enabled products also create new testing problems.
A deterministic calculation may have one correct result. A generative system can produce several acceptable answers.
Depending on the product, teams may need evaluations for factual accuracy, task completion, unsafe output, retrieval quality, prompt-injection resistance, latency, cost, and regressions after a model or prompt change.
The core engineering rule remains useful:
Define the failure that matters, then build the cheapest reliable way to detect it.
Good QA gives a CTO evidence about release risk
The commercial value of QA is not the number of test cases written.
For an engineering leader, the useful question is:
What can this release break, and what evidence tells us the main risks are controlled?
A strong answer should connect tests to architecture.
Critical business rules have checks at the right layer. Important external failures can be reproduced. CI gates catch regressions early. High-risk user journeys have end-to-end coverage where it adds value. Known gaps remain visible instead of being hidden behind a green dashboard.
Code & Pepper’s project work shows how different that can look in practice.
DirectID used unit and integration testing around a three-layer Azure architecture and retry behaviour. Dinarak reports 90% test coverage for its mobile payments application. Smart Pension combined more than 20 branch merges per day with nearly 100% test coverage.
Different products need different QA systems.
If developers are losing time to manual regression, external failures are difficult to reproduce, or the existing automation suite cannot be trusted, the first task is not to add more tests. It is to identify the most expensive failure paths and put reliable checks around them.
That is where Code & Pepper’s QA engineering team can help: audit the current testing approach, identify gaps, and build quality checks around the risks that matter to the product.
What does a QA engineer do?
A QA engineer identifies software risks and creates repeatable ways to detect them. The work can include requirements review, exploratory testing, API testing, automation, integration testing, defect analysis, CI/CD, and release assessment.
Is QA the same as software testing?
No. Testing is one part of QA. Quality assurance also covers requirements, risk analysis, test strategy, testability, automation design, process improvement, and release controls.
Does a QA engineer need to code?
Coding is important for automation-heavy QA roles. It helps engineers test APIs, build maintainable automation, query data, integrate checks with CI/CD, and debug failures. The required depth depends on the product and role.
Which QA tools should a team use?
Choose tools according to the testing layer and existing stack. Playwright and Cypress are common choices for browser automation. Postman and code-based frameworks can test APIs. Tools such as k6 and Artillery can support performance testing.
How much does a QA engineer earn in the US?
The US Bureau of Labor Statistics reports a median annual wage of $104,300 for software quality assurance analysts and testers in May 2025. The mean annual wage was $111,490.
How long does test automation take?
There is no universal implementation time. A useful estimate needs the current architecture, test coverage, environments, test data, CI/CD setup, external dependencies, and target scope. A proof of concept and a production regression system should not share the same estimate.
Should developers or QA engineers write automated tests?
Both can. Developers often own tests close to their code. QA engineers can add broader API, integration, regression, end-to-end, performance, and risk-focused checks. Ownership should follow the architecture and team skills.
Is 100% test coverage a good target?
Not by itself. Coverage shows which code ran during testing. It does not prove that tests contain useful assertions or cover the highest-risk failure modes.