QA Skills Matrix_

Has basic theoretical knowledge of what estimation and prioritization are: able to estimate simple tasks and explain them

Requests task priorities from N+1

Knows types of test documents and their purpose

Escalates in case of blockers or wrong estimations

Has practical testing experience and understanding of the testing process

Can read and understand the current test plan (their own activities and the current approach) with some guidelines

Able to estimate non-complex tasks with an acceptable level of precision (in hours, story points, etc.)

Has an understanding of task prioritization and decomposition

Clearly understands the difference between severity and priority

Can read and understand Test Strategy and Test Plan

Can actualize or contribute to an existing Test Plan

Provides estimation for tasks of medium complexity level

Able to identify major risks and report them to a person responsible for risk management

Able to make decomposition and estimation of complicated tasks

Autonomously prioritizes and estimates tasks of high complexity

Able to do high-level estimation for projects and knows different approaches

Can create test documentation (Test Plan, Test Strategy) using best practices

Able to identify and describe risks, and provide a mitigation plan

Knows and uses different estimation approaches (expert, work breakdown, weighted, 3-point)

Can independently create from scratch Test Plan, Test Cases, Test Suite, CheckList, Test Report, etc.

Has experience in planning and estimating a complete set of testing activities on 3+ projects

Can prioritize testing tasks for the project and convince the customer

Can identify quality risks and provide a plan to mitigate them, and has real-life risk-based testing experience on 2+ projects

Knows what ROI is and has calculated it 2+ times

Knows formal models of process improvement and is able to apply them (like TMMi, TPI, and ASPICE)

Created at least 5+ test plans/test strategies, agreed with the customer, and used them in testing

Able to explain and sell the benefits of the selected testing approach to stakeholders

Knows the structure of Test Cases and their difference from Checklists

Knows the main Test Design Techniques (Equivalence Partitioning (EP), Boundary Value (BV), Decision Table (DT), State-Transition (ST)) - knows 1 or 2

Can create Test Cases and Checklists with help and directions from senior colleagues on positive and some negative scenarios

Knows the differences between:

Understands the most commonly used test design techniques (EP, BV, DT, ST) and uses common test-design approaches when choosing test data

Can do test design of readable test cases on positive and negative scenarios

Knows how to investigate the issue and can provide examples of:

Able to identify and balance test coverage

Understands the concept of test levels and test types

Able to identify which Test Cases are more reasonable to put at each test level

Can do test design on positive and all negative scenarios

Knows the IEEE standard for test design (29119 p4)

Uses the most common test design techniques (EP, BV, DT, ST)

Can define test data

Able to approach non-functional aspects of the product by suggesting NFT approaches

Can review test cases/checklists of colleagues and mentor/consult them, has relevant experience

Able to perform test design based on given requirements, or according to common sense and experience if exact requirements are not provided

Optimizes the amount of test data required for Test Case execution

Involves a risk-based approach in test design

As a plus, has a preferred approach to test analysis

Identifies which test design technique could be used in a specific case, based on experience and risks

Supervises colleagues on different test design techniques and optimal approaches

As a plus, has their own approach to test analysis and test design which is proven in 3+ projects

Has basic theoretical knowledge of test reporting.

Can report their own activities for the day, week, and sprint (tests created, tests executed, bugs created, other activities).

Understands 1st level metrics (number): # of defects, # of TCs passed/failed, # of defects by their criticality, etc.

Can explain what Test Coverage, Test Effort, Defect Distribution, Test Execution, Requirements, and Requirement Coverage are, etc.

Able to supply a report based on a given template

Able to provide a clear structural status for testing activities under their own responsibility.

Able to read a report and understand the current product quality.

Understands 1st and 2nd level metrics (relative): pass/fail rate per module/feature, defect leakage rate (e.g., found on PROD/total found * 100%), etc.

Creates different diagrams to illustrate the quality of testing using metrics.

Provides a clear high and low-level structural overview of the testing status for the project.

Able to differentiate the details of reports depending on the level of the target audience.

Reports product quality.

Provides certain, precise, narrative, and easy-to-understand information in reports.

Regularly collects metrics in daily work, analyzes existing ones for improvements, and implements them for practical usage.

Understands 1st, 2nd, and 3rd level metrics (distribution): defect leakage dynamics over iterations, dependency between the number of defects and the number of changes over iterations, etc.

Collects information for metrics from different sources.

Able to define reporting standards and templates applicable for different deliveries, and supervise others on the subject.

Has their own approach and style to report/present the quality, proven with good feedback in 3+ projects.

Highlights the key things and gives a quick understanding of the essence of the information.

Assures that reports highlight only valuable information.

Can apply 1st, 2nd, and 3rd level metrics to the current project, and understands well how to collect and use them.

Identifies gaps in the reporting process and provides approaches based on test metrics for improving the QA process and quality of the tested application/product.

Has practical experience of metrics alignment with stakeholders, and implementation in 3+ projects.

Has basic theoretical knowledge of SDLC and the concept of environments

Has theoretical knowledge of Traditional and Agile SDLCs, and practical experience (with at least one)

Knows the stages of the SDLC and can describe the activities for each stage with examples from practice

Understands what the build and deployment process is

Can describe the advantages and disadvantages of different SDLC models

Able to describe each stage of a project-specific SDLC in detail

Knows which environments are used for what

Understands principles of environment and configuration management

Knows common test activities and artifacts for each SDLC stage

Can set up build/deploy/test environments and test tools

Has strong knowledge of various SDLCs (knows the pros and cons of each, benefits, challenges), and has wide practical experience

Has knowledge about the environment lifecycle, from creation to shutdown

Able to use CI/CD tools, configure them, and set up new flows

Manages test infrastructure, holds responsibility for the full testing lifecycle

Identifies gaps in the process, and proposes new approaches to increase speed/quality or reduce resource utilization

Can introduce methodology practices

Participates in SDLC selection for the project, can revise processes and suggest effective ideas for improvements, and works in continuous improvement mode

Able to adjust the QA process to the current specific SDLC, including project-specific options

Can set up the QA process starting from static testing and ending with test closure activities

As a plus, experience and skills are enough to set up agile/scrum project processes and ceremonies from scratch

Has theoretical knowledge of the structure of a bug and its life cycle

Able to create a bug report (fill in all required fields), read it, and reproduce the issue in the tracking system

Knows the difference between verification and validation

Able to create a structured bug report with all required information

Understands the difference between severity and priority

Can perform defects localization

Uses available tools to investigate the issue (logs, DBs, console, and DevTools)

Understands software structure and is able to assume root cause reasons

Able to identify reasonable priority/severity

Able to create a structured bug report (additionally provides bug meta-information: links, tags, builds, modules, children, parents, and details like logs, screenshots, used test data, etc.)

Can investigate the issue and localize a bug without supervision

Knows tools to assist with root cause analysis

Able to analyze the impact of priority/severity

Can localize defects in integrated systems

Understands software structure and can investigate the root cause

Cooperates with the development team for analysis, follows up on issue fixing progress

Can read poorly described bug reports and reproduce the issue

Performs systematization of defects at the end of an iteration

Does deep level investigation for the root cause; defines and mitigates potential weak areas ahead, etc.

Participates in business issues investigation. Provides full information about the bug location, including code references and related issues from the past

Able to perform localization in an unknown environment by collecting needed information

Has a deep understanding of systems logic and defect localization principles

Performs systematization of defects at the end of an iteration to contribute to lessons learned

Has basic theoretical knowledge of what a requirement is and its characteristics. Able to read and understand project requirements.

Has experience in requirements testing.

Can analyze major aspects of a requirement, such as unambiguity, logic, testability, actuality, potential test cases, and integrations.

Can analyze different types of requirements: user stories, UML diagrams, flow charts, SRS, and FRS.

Knows the main criteria for requirements defined by IEEE and can match requirements to IEEE criteria.

Provides requirements and specification review and analysis.

Establishes traceability between requirements and other project artifacts (bugs, TCs, code commits, release notes, etc.)

Able to analyze dependencies and risks, provide their own vision on weak points, and propose improvements.

Able to write less formal requirements (e.g., use cases).

Able to work with project-level requirements.

Able to work with low-quality requirements.

Provides high-quality requirements and specification review and analysis.

Has basic theoretical knowledge of what DBs are. Knows the most common databases.

Understands the data testing process.

Has experience with MS Excel and knows the main commands.

Understands different data types and is able to define appropriate test ranges under supervision.

Understands and can analyze a simple data set.

Can perform simple select queries on a DB.

Knows common file types such as txt, xlsx, and csv.

Able to manipulate data with basic tooling functions for analysis.

Able to define clear and optimal data sets and scenarios for data quality testing.

Can analyze and test data flow, transformation, and loading processes (ETL).

Familiar with at least one database management system.

Familiar with Excel pivot tables and charts.

Performs the analysis of large, complex data sets in short timeframes using optimal approaches and provides valuable results.

Able to analyze the data workflow and find bottlenecks.

Understands the impact of data structure usage to identify the algorithmic complexity of processing and common defects that appear because of it.

Able to generate information using specific data structures as a target, choosing optimal ways to process and save the data.

Able to analyze the data workflow and find bottlenecks and provide their own vision on how to improve it.

Understands the internal architecture of the database and how its engine works.

Able to generate information using specific data structures as a target, choosing optimal ways to process and save the data.

Optional as a plus: knows basic information about Webservices, Web technologies, client-server architecture (how back end works with front end and vice versa), OS, UNIX, SQL, API, REST, Web

Basic understanding of how software is created and how it works

Knows how to use project-specific tools in order to execute test activities (any single tech: OS, UNIX, SQL, API, REST, Web)

MS Office or Google Docs or command line, etc.

Knows how to work with any developer tools console

If applicable - SQL: CRUD

Able to explain technology, when it can and should be used/tested, how it works (OS, UNIX, SQL, API, REST, Web)

Knows how to use test-related tools (Task-management, Test Case Management, Defect Management tools)

Has a knowledge of domain-specific technologies (FE and/or BE), protocols, logging, DBs, build tools, version-control tools

Understands types of software (client-server, desktop, web, console, etc.)

Basic understanding of how OS works (programs, services, files, access)

Able to solve tasks using any Dev tools console

If applicable - SQL: can create SELECT queries, knows JOINs and UNIONs

Proficient with workflows inside test management tools, including custom workflow configuration and permissions assignment (Task-management, Test Case Management, Defect Management tools). Able to replicate a real project lifecycle within those tools.

Has advanced knowledge of BE/FE surroundings (APIs, SPAs, Logging, DBs, OS, SCM, common architectural approaches)

Has basic programming skills in any language (Basic knowledge of OOP, at least able to read code)

If applicable - has good knowledge of working with REST Clients like Postman, Fiddler

If applicable - knowledge of SQL queries (nested queries, SQL functions, etc.)

If applicable - knows performance and security testing tools

Has architectural knowledge, aware of algorithms, tools, conventions developers use

Has basic knowledge about Dev framework structure, where components are stored, integration approaches

Proficient with DBs, Logging, network protocols

Knows and uses different tools to work with technology. Knows alternatives and can compare/evaluate

Defines testing tools for projects and the level of testing depth

Understands what AI assistants and LLMs are, what prompts are, and why outputs are non-deterministic.

Can use approved AI tools to ask testing questions and explain basic code or testing concepts.

Understands basic risks: hallucinations, confidentiality, and the need to verify outputs.

Uses AI to draft simple test cases, checklists, test data, scripts, bug reports, and test documentation.

Uses AI to analyze software requirements, user data, and scenarios.

Uses AI to understand unfamiliar code, logs, or business domain concepts.

Validates AI-generated artifacts before use and follows organizational policy for safe data handling.

Independently applies structured prompting techniques such as context provisioning, examples, decomposition, and response constraints.

Uses AI in recurring QE workflows: scenario generation, data preparation, automation support, test maintenance, flaky test triage, and log/root-cause analysis.

Evaluates AI tools for accuracy, privacy, cost, and practical fit for the team.

Uses AI safely within approved workflows and can explain where human review is still required.

Designs an AI-augmented testing approach for a product or team.

Customizes assistants, knowledge bases, or bounded agents for project context.

Integrates AI-enabled checks into the delivery workflow where appropriate, such as triage, maintenance, failure analysis, or prioritization.

Defines team guardrails for safe usage and mentors others.

Measures impact using concrete outcomes such as cycle-time reduction, flake reduction, maintenance effort, or defect detection improvement.

Defines organization-wide strategy and standards for AI use in Quality Engineering.

Selects, builds, or scales enterprise AI capabilities for QE.

Establishes governance, security, measurement, and adoption patterns across teams.

Drives innovation in agentic quality workflows with clear human oversight and risk controls.

Understands the difference between deterministic systems and probabilistic AI systems.

Knows basic terminology: model, training data, inference, prompt, hallucination.

Can identify AI components in a system under test and explain why classic test oracles may need adaptation.

Executes functional and exploratory tests against AI features using predefined or simple user scenarios.

Uses reference examples or labeled data when available.

Identifies and documents AI-specific defects clearly, including hallucinations, unsafe responses, poor grounding, or unstable formatting/behavior.

Understands basic evaluation concepts and common risks such as inaccurate output, harmful content, or sensitive data exposure.

Designs tests for probabilistic systems using tolerances, rubrics, datasets, or statistical checks where appropriate.

Tests RAG/LLM features for retrieval quality, relevance, faithfulness, and hallucination behavior.

Tests data pipelines and regression after model, prompt, retrieval, or configuration changes.

Covers edge cases, noisy inputs, robustness, fairness concerns, and integration points with the rest of the system.

Participates in experiments such as A/B comparisons and can interpret task-appropriate metrics.

Designs an end-to-end AI test strategy covering model, data, prompts, retrieval, tools/agents, integrations, and production monitoring.

Builds or operates automated evaluation pipelines using human rubrics and, where appropriate, LLM-as-a-judge.

Defines quality gates for release or promotion using task-appropriate metrics and risk-based thresholds.

Performs adversarial testing and red teaming for prompt injection, jailbreaking, insecure tool use, PII leakage, data poisoning, abuse, and misuse scenarios.

Includes non-functional concerns such as latency, throughput, token usage, cost, reliability, and drift.

Establishes organization-wide AI assurance practices covering governance, continuous evaluation, auditability, incident learning, and human oversight.

Aligns AI quality practices with enterprise risk frameworks, applicable standards, and regulation.

Leads quality strategy for advanced AI systems including agentic and multimodal systems.

Coaches senior engineers and helps shape cross-functional practices with product, platform, security, data, and legal teams.