These skills do not affect the main grade but show the breadth of your profile.
Agile Methods
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Understands the fundamentals of SAFe Framework. Applies basic practices in daily work. Follows recommendations from the team and documentation.
Actively participates in SAFe ceremonies: PI Planning, System Demo, Inspect & Adapt. Coordinates product backlog with ART backlog. Manages features and enablers considering technical dependencies and architectural runway.
Performs the Product Manager role in ART. Defines program backlog balancing business features and technical enablers. Coordinates solution intent with Solution Architect. Facilitates PI Planning for 5-10 teams.
Shapes SAFe implementation strategy for the division. Defines Value Streams and ARTs with product-centric lens. Introduces Lean Portfolio Management. Trains TPMs on working in SAFe environments.
AI Coding Assistants
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Knows ChatGPT and Claude capabilities for product work. Uses AI assistants for idea generation, specification writing and data analysis. Understands LLM limitations in the context of technical tasks.
Applies ChatGPT/Claude for accelerating product work: competitor analysis, user story generation, specification code review. Creates effective prompts for technical tasks. Evaluates AI-generated content quality.
Integrates AI tools into the team's product workflow. Defines use cases for AI assistance in technical planning, architecture review, and documentation. Creates custom prompts for recurring tasks.
Shapes AI tool usage strategy for the product division. Defines guidelines for quality and security of AI-generated content. Introduces AI copilot practices for the TPM team.
Knows GitHub Copilot capabilities for accelerating development. Understands how AI code assistants affect team productivity. Can evaluate potential ROI from Copilot adoption for their team.
Evaluates GitHub Copilot impact on feature delivery speed and code quality. Analyzes adoption metrics — acceptance rate, time saved, code quality impact. Formulates recommendations for the team on effective usage.
Defines AI coding assistant adoption strategy for the product. Evaluates security and compliance risks of Copilot usage. Measures impact on delivery metrics and incorporates into product planning.
Shapes AI coding tool usage policy for the division. Defines guidelines for security, IP and compliance for Copilot/Codeium. Evaluates aggregate impact on engineering productivity and quality.
Algorithms & Data Structures
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Understands basic algorithm complexity concepts O(n), O(n²) for evaluating impact on user experience. Can assess when a slow feature is an algorithm problem rather than an infrastructure issue.
Uses understanding of algorithmic complexity for prioritizing optimizations — which features scale with data growth, where bottlenecks occur as product user base grows.
Makes product decisions considering algorithmic constraints — choosing between accuracy and search speed, trade-offs in recommendation systems, evaluating feature scalability at 10x growth.
Embeds algorithmic complexity analysis into the product decision-making process. Ensures technical constraints are considered at discovery stage rather than during development.
Understands main data structures — arrays, objects, trees — at a level sufficient for reading technical documentation. Can discuss product data model choices with developers.
Understands data structures well enough to evaluate team technical proposals — impact of storage choices on performance, memory volume and feature implementation speed.
Uses data structure knowledge for product decisions — choosing between normalization and denormalization, evaluating trade-offs between read and write speed when designing features.
Participates in architectural discussions on equal terms with tech leads, justifying product requirements considering data structure constraints. Translates business needs into technical specifications.
Application Security
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Knows main OWASP Top 10 vulnerability types and understands their potential impact on the product and users. Can include security requirements in user stories.
Considers OWASP recommendations when designing features — secure authentication, user data protection, input validation. Prioritizes security tasks in the product backlog.
Integrates security into the product process — threat modeling at the discovery stage, security requirements in definition of done, regular audits. Evaluates business risks of vulnerabilities.
Defines product security strategy — compliance requirements, personal data processing policy, incident response process. Communicates risks to stakeholders.
CI/CD
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Understands the CI/CD concept and how GitHub Actions automates the product delivery process. Knows main pipeline stages — build, testing, deploy — and their impact on release speed.
Uses GitHub Actions capabilities for improving the product process — automatic preview environment deployment, build status notifications, integration with project boards and issue tracking.
Optimizes CI/CD pipeline to accelerate product delivery — parallel checks, feature branch deployments, automated rollbacks. Measures and improves team's DORA metrics.
Defines CI/CD strategy for the product team — deployment frequency, change failure rate, lead time. Ensures the pipeline supports the required iteration speed.
Clean Code & Refactoring
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Understands the importance of code quality for product delivery speed. Knows basic metrics — test coverage, cyclomatic complexity — and can interpret SonarQube reports.
Considers code quality metrics when planning sprints — allocates time for refactoring, tracks technical debt trends. Uses data to justify quality investments.
Integrates code quality metrics into the product process — quality gates before releases, automated degradation monitoring. Balances feature delivery speed with codebase health maintenance.
Defines code quality policy for the product team — acceptance standards, SLA for critical defect resolution, technical debt budget. Communicates quality value to stakeholders.
Cloud Providers
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Understands main AWS services and their role in product infrastructure. Knows that EC2, S3, RDS are used for hosting, storage and databases, affecting cost and scalability.
Evaluates cloud architecture impact on product cost and performance. Participates in AWS service selection — Lambda vs ECS, DynamoDB vs RDS — from business requirements perspective.
Makes architectural decisions on product cloud infrastructure — multi-regional deployment, DR strategy, cost optimization. Balances reliability and budget.
Defines cloud strategy for the product team. Manages AWS budget, makes decisions on reserved instances vs on-demand, ensures compliance and cost optimization.
Concurrency & Parallelism
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Understands the asynchrony concept and its impact on user experience — data loading without blocking UI, background operations. Knows basic terminology for communicating with the team.
Considers async patterns when designing features — optimistic updates, background processing, task queues. Understands the impact of asynchrony on UX and designs loading states.
Makes product decisions considering asynchronous architecture — event-driven features, real-time updates, eventual consistency for users. Designs graceful degradation scenarios.
Defines standards for async operation handling in the product — UX patterns for long-running tasks, notification strategy, processing priorities. Ensures consistent user experience.
Containerization
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Understands the containerization concept and why Docker is used in product development. Can launch a local development environment from docker-compose for feature testing.
Uses Docker for reproducing bugs and testing features in isolated environments. Understands how containerization affects the deployment process and product update delivery speed.
Considers container architecture when designing product solutions — microservice decomposition, independent feature deployment, feature flags. Evaluates impact on time-to-market.
Defines Docker usage standards in the product process — preview environments for stakeholders, automated testing in CI, UAT environments. Optimizes the delivery pipeline.
Git & Workflows
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Understands basic Git advanced concepts for technical product management: branching strategies (GitFlow, trunk-based) for release planning, tag-based versioning for product releases, merge conflict resolution awareness for sprint planning. Follows team conventions for release branch management.
Confidently works with Git for tracking technical changes: reviews diffs, understands merge conflicts and branching strategies. Reads git log for velocity and change pattern analysis. Configures branch protection rules.
Defines branching strategy for the product considering release cadence and feature flags. Analyzes git history to identify tech debt hot spots. Configures automated workflows through GitHub Actions for product processes.
Shapes Git workflow standards for the division: trunk-based development, release branching, monorepo strategies. Introduces automated release notes generation. Defines change management process for critical systems.
Integration Testing
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Understands the role of integration testing for product stability. Knows what is verified at this level — component interactions, API contracts, data flow scenarios.
Considers integration testing when planning features — identifies critical integration points, allocates testing time in estimates. Tracks regressions between components.
Defines integration testing strategy for the product — which scenarios are critical, which can be covered by contract tests, where full e2e checks are needed. Prioritizes by business risk.
Shapes integration testing policy for the product team. Ensures balance between testing thoroughness and delivery speed, introduces integration quality metrics.
Logging
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Understands the role of logging for diagnosing product issues. Knows how logs help identify bug causes, track user behavior and evaluate feature performance.
Uses logs for analyzing product metrics — conversion funnels, error frequency, key operation execution time. Formulates logging requirements for new features.
Designs product observability requirements — which events to log, which metrics to collect, how to correlate technical and business indicators. Embeds observability into the process.
Defines logging and monitoring strategy for the product team. Ensures every feature ships with success metrics and dashboards for tracking business impact.
Metrics & Monitoring
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Understands basic Prometheus & Grafana for product management: reading product health dashboards (availability, latency), understanding SLI/SLO metrics for product reliability discussions, navigating Grafana panels for user-facing service metrics. Follows team conventions for product health monitoring.
Formulates product metric monitoring requirements — conversion, key operation latency, error rate. Creates Grafana dashboards for tracking business KPIs in real-time.
Designs product monitoring system — SLI/SLO for key user scenarios, alerts on business metric degradation, correlation of technical and product indicators.
Defines monitoring strategy for the product team. Establishes SLOs for the product, ensures visibility for stakeholders and configures escalation process on SLA violations.
OOP & Design Patterns
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Knows main design patterns at a level sufficient to understand team technical discussions. Understands why patterns are important for product extensibility and maintainability.
Uses pattern knowledge for evaluating technical decisions — strategy for A/B tests, observer for notifications, factory for creating different content types in the product.
Applies pattern knowledge when designing product solutions — plugin architecture for extensions, CQRS for reporting, saga for complex business processes with compensations.
Evaluates team architectural decisions from a product strategy perspective. Ensures chosen patterns support planned functionality and product scaling.
Understands basic OOP principles at a level sufficient for reading technical specifications and UML diagrams. Can participate in product module architecture discussions with developers.
Uses OOP understanding for evaluating team technical decisions — code modularity, system extensibility, cost of adding new features. Understands when architecture slows product development.
Applies OOP principles knowledge to product decisions — monolith decomposition, designing extensible APIs, evaluating the cost of maintaining backward compatibility.
Participates in architectural decisions, justifying product requirements for modularity and extensibility. Ensures architecture supports the planned roadmap 2-3 quarters ahead.
Prompt Engineering
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Uses AI tools for technical specification drafting and architecture documentation. Formulates prompts that bridge business requirements and technical implementation details. Leverages AI for API design reviews and technical debt assessment.
Applies advanced prompt engineering techniques: chain-of-thought, structured output, system prompts. Creates prompts for technical tasks: specification generation, architecture analysis, code review guidance.
Defines prompt engineering practices for the product team. Creates a prompt library for typical TPM workflows. Evaluates quality and reliability of prompts. Integrates AI capabilities into product processes.
Shapes prompt engineering standards for the division. Introduces prompt testing and evaluation framework. Defines best practices for AI-assisted product management. Trains TPMs on effective LLM usage.
Relational Databases
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Understands basic PostgreSQL concepts for product management: database performance impact on product SLAs, migration planning for feature releases, recognizing data model constraints in feature discussions. Follows team conventions for database-related product decision documentation.
Works with PostgreSQL for product data analysis — writes queries for analytics, understands database schema impact on feature performance. Considers constraints when designing the product.
Makes product data model decisions leveraging PostgreSQL capabilities — JSONB for flexible structures, partitioning for scaling, full-text search for content search.
Defines data storage strategy for the product team. Coordinates database schema migrations with product roadmap, ensures backward compatibility and downtime planning.
Unit Testing
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Understands the value of unit testing for product quality and development speed. Knows how test coverage affects release stability and team confidence when changing code.
Considers testing when planning sprints — allocates time for test writing, tracks coverage of critical business scenarios. Justifies test investments through defect metrics.
Integrates testing metrics into the product process — quality gates, regression trends, correlation of coverage with bug frequency. Defines testing priorities based on business risks.
Defines testing policy for the product team — minimum coverage for release, testing strategy for new features vs legacy code. Balances delivery speed with reliability.
User Documentation
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Understands the importance of structured knowledge management for a technical product. Can create and maintain documents in Confluence, Notion or similar tools. Knows principles of knowledge base organization for a technical team.
Independently designs knowledge base structure for the product: architecture docs, runbooks, decision logs. Defines information architecture for different audiences — developers, TPMs, stakeholders. Maintains freshness.
Builds knowledge management strategy for the product. Implements docs-as-code approach for technical documentation. Defines lifecycle management for documents: creation, review, deprecation. Integrates with dev workflows.
Defines knowledge management standards for the division. Introduces cross-team knowledge sharing practices. Shapes information architecture for 10+ products. Evaluates knowledge base health metrics.
Understands the role of user documentation for a technical product. Knows documentation types: quickstart guides, tutorials, reference docs, FAQs. Can review documentation for accuracy and completeness.
Defines user documentation requirements for a technical product. Coordinates documentation creation for developer and end-user audiences. Reviews documentation for technical accuracy and user-friendliness.
Shapes documentation strategy for the product. Defines content architecture considering user journeys. Implements metrics: time-to-resolution, doc usage, search success rate. Coordinates docs with product releases.
Defines documentation standards for the division. Introduces docs-as-product approach with user research and iteration. Shapes documentation team structure and processes. Measures documentation quality with customer feedback.