Understanding Technical Skills: What Companies Actually Evaluate

A comprehensive guide to the technical skills companies evaluate in interviews, including specific concerns, strengths, and progression expectations for different SDE levels.

Purpose

This guide was created to address three critical needs:

• I need to understand what companies actually evaluate: Learn the specific technical skills and behaviors that determine interview success
• I need to prepare effectively for technical interviews: Focus on the most impactful behaviors and avoid common pitfalls
• I need to demonstrate technical competence: Showcase the right combination of skills that companies value most

The goal is to transform technical interview preparation from guesswork into a systematic, evidence-based approach.

Why Technical Skills Matter

Companies evaluate technical skills to ensure candidates can:

• Deliver Quality Code: Write maintainable, efficient, and scalable solutions
• Solve Complex Problems: Break down ambiguous problems into actionable solutions
• Design Systems: Create architectures that can scale and evolve
• Make Sound Decisions: Choose appropriate technologies and approaches
• Handle Ambiguity: Work effectively when requirements are unclear
• Influence Stakeholders: Communicate technical concepts to non-technical audiences

Core Technical Skills Companies Evaluate

Writing Clean Code

Writing Clean Code

Write syntactically-correct code that is logical, well-organized, easy to understand, works as intended, and is maintainable.

Concerning Behaviors	Strength Behaviors
Code contains pseudo-code or doesn't work for basic use cases	Writes syntactically correct code that works as intended
Creates overly complex code with improper coding constructs	Creates simple, clean, and efficient code with proper coding constructs
Writes unstructured (spaghetti) code with obscure operations	Writes well-structured, logical code with clear operations
Creates difficult to maintain code with complex if statements	Creates maintainable code by breaking complex logic into functions
Code is difficult to read and understand	Code is organized, easy to read, and clearly communicates intent
Creates monolithic functions with complex signatures	Breaks algorithms into logical functions with clean boundaries
Writes code that is not easy to test or debug	Writes code that is easy to test and debug
Lacks code reuse and uses improper globals	Leverages reuse with helper functions and appropriate scope
Doesn't consider code reuse and has poor formatting	Leverages code reuse and maintains consistent formatting
Uses poor variable naming conventions	Uses clear, descriptive variable naming conventions
Does not consider extensibility for future requirements	Identifies areas where requirements may evolve and designs for extensibility
Creates code that requires significant rewrites for new requirements	Designs code that can accommodate new requirements with minimal changes

Key Interview Questions:

• "Walk me through your approach to writing clean, maintainable code"
• "How do you handle code reviews and feedback?"
• "Tell me about a time you had to refactor legacy code"
• "How do you ensure your code is testable and debuggable?"
• "Would someone understand what you're trying to accomplish by reading your code?"
• "What would happen if new requirements were added to your solution? Would it require a significant rewrite?"
• "How do you ensure your code is logical and maintainable?"
• "Can you show me a simpler way to accomplish this task?"

Data Structures & Algorithms

Data Structures & Algorithms

Uses optimal data structures and algorithms to solve problems, taking into account tradeoffs, limitations, and constraints.

Concerning Behaviors	Strength Behaviors
Unable to identify appropriate data structures or algorithms	Identifies and implements the right data structures to solve problems effectively
Does not progress past brute force solutions	Reaches optimized solutions through systematic analysis of multiple approaches
Cannot justify why specific data structures and algorithms were chosen	Justifies why selected data structures and algorithms were used with clear reasoning
Unable to calculate runtime and space complexity	Demonstrates understanding of time and space complexities for different approaches
Cannot recognize tradeoffs and optimizations	Demonstrates solid grasp of runtime and space complexity tradeoffs
Does not understand shortcomings of different approaches	Understands and communicates trade-offs of different data structures and algorithms
Cannot identify potential shortcomings and discuss tradeoffs	Identifies potential shortcomings and discusses tradeoffs with different approaches
Cannot compare different solutions effectively	Compares multiple solutions and explains why one is more optimal than the rest
Fails to consider common data structures beyond basic ones	Considers common data structures including ones not regularly used
Does not ask clarifying questions about problem constraints	Asks the right questions to understand problem constraints before choosing approach
Cannot explain approach and reasoning clearly	Explains approach and thinking/reasoning behind data structure and algorithm choices
Lacks knowledge of fundamental algorithms like BFS/DFS	Demonstrates knowledge of fundamental algorithms like BFS and DFS and when to use each

Key Interview Questions:

• "Explain the time and space complexity of your solution"
• "What alternative data structures could you use and what are the tradeoffs?"
• "How would you optimize this solution for better performance?"
• "Walk me through your thought process for choosing this algorithm"
• "How would you identify and implement the right data structure to solve this problem?"
• "What are some common data structures you could use here, including ones you might not use regularly?"
• "Consider different data structures - is one more optimal than the rest for this specific problem?"
• "Can you explain your approach and reasoning behind choosing this data structure?"
• "What questions would you ask to better understand the problem constraints before choosing your approach?"
• "How would you implement BFS and DFS algorithms for this problem, and when would you use each?"

Problem Solving

Problem Solving

Solve real-world coding problems by clarifying and precisely defining the problem and choosing the best approach, taking into account complexities, constraints, and tradeoffs.

Concerning Behaviors	Strength Behaviors
Does not solve the problem or fails to meet requirements	Solves problems meeting all given requirements
Solves only part of the problem and cannot expand when prompted	Solves complete problems and can expand when requirements change
Does not progress past brute force solutions	Reaches optimized solutions through systematic analysis
Creates fragile solutions that don't handle edge cases	Creates robust solutions that handle edge cases and error conditions
Does not consider impact of errors on customers or components	Considers impact of errors on customers and other system components
Begins solving without asking relevant clarifying questions	Defines scope by asking relevant clarifying questions
Does not justify decisions with regard to technical requirements	Provides justification for decisions with regard to technical requirements
Commits to initial solution without explaining alternatives	Considers multiple approaches and explains why one solution is better
Does not compare different solutions or explain tradeoffs	Justifies decisions by identifying tradeoffs with several different solutions
Requires excessive hints or guidance to see solutions	Requires minimal hints and uses feedback to reach solutions
Unable to change approach based on changes to the problem	Able to change approach based on changes to the problem
Does not consider additional factors beyond basic problem	Considers additional factors (developer effort, team composition) beyond basic problem
Does not consider alternatives or respond to feedback	Considers alternatives and responds constructively to feedback
Starts coding before clearly describing the solution	Clearly describes solution before beginning implementation
Stays at high level and dismisses concerns about details	Balances high-level thinking with attention to important details
Does not commit to an approach and relies on interviewer	Commits to approaches and drives implementation decisions

Key Interview Questions:

• "How would you approach this problem if the requirements were different?"
• "What clarifying questions would you ask before starting to solve this?"
• "How would you handle edge cases and error conditions?"
• "Tell me about a time you had to change your approach mid-problem"

System Design & Architecture

System Design & Architecture

Design software solutions to enable new features or improve systems, making technologically appropriate decisions in the context of broader business and technology strategy.

Concerning Behaviors	Strength Behaviors
Solution does not meet core functional requirements	Solution meets identified requirements efficiently
Fails to describe a viable, coherent, and complete design	Drives design discussion with logical progression from problem to solution
Solution introduces significant and avoidable complexity	Solution minimizes complexity while meeting requirements
Solution may not function reliably under environmental changes	Solution incorporates fault-tolerance and monitoring elements
Fails to validate functional requirements within context	Identifies all requirements necessary for a working solution
Does not capture basic customer use cases or scaling considerations	Identifies edge cases and articulates impact of technical requirements
Does not consider impact on other components in the system	Considers impact on other components and system integration
Solution creates avoidable maintenance challenges	Solution minimizes maintenance effort and costs
Fails to clarify requirements or manage ambiguity	Removes ambiguity by identifying implicit requirements
Does not clarify scope of design or customer behaviors	Identifies key technical decisions and asks clarifying questions
Unable to break down the problem into functional components	Breaks down problems into manageable functional components
Identifies requirements that are unclear or ineffective	Clarifies requirements as needed and makes reasonable assumptions
Does not consider performance or operational excellence	Extends design beyond functional needs to performance, security, scaling
Fails to contemplate multiple design choices	Identifies potential shortcomings and tradeoffs with different designs
Fails to identify tradeoff opportunities	Incorporates intentional tradeoff decisions supporting customer requirements
Does not consider shortcomings of proposed solution	Articulates cases for and against different approaches
Does not employ existing constructs to simplify design	Recognizes and discusses use of design patterns and industry standards
Fails to consider scalability implications of design decisions	Designs systems that can scale and handles growth considerations
Does not consider downstream impact of component changes	Anticipates and designs for downstream implications of system changes
Fails to plan for testing and validation of the system	Incorporates comprehensive testing strategies and validation approaches

Key Interview Questions:

• "How would you design this system to handle 10x the current load?"
• "What are the key tradeoffs in your design approach?"
• "How would you handle failure scenarios and ensure reliability?"
• "Walk me through how you would scale this system over time"
• "Can this system scale? What are the bottlenecks and how would you address them?"
• "What are the downstream implications if parts of your solution are altered?"
• "How would you test this system to make sure it works under various conditions?"
• "What would happen if new requirements were added? Would it require significant architectural changes?"

Database Design

Database Design

Design and plan the structure and organization of data in a database. Includes using data modeling techniques to identify and define entities, attributes, and relationships, and making trade-offs between normalization and denormalization to optimize performance and usability.

Concerning Behaviors	Strength Behaviors
Makes significant errors impacting data integrity and scalability	Produces high-quality database designs ensuring data integrity and efficiency
Makes suboptimal trade-offs leading to performance issues	Optimizes database performance, usability, and scalability through efficient designs
Shows difficulty in optimizing database performance	Makes informed decisions about normalization vs. denormalization trade-offs
Struggles to identify and define entities, attributes, and relationships	Applies data modeling techniques effectively to identify entities and relationships
Fails to consider trade-offs between normalization and denormalization	Communicates database design decisions clearly and effectively
Fails to communicate database design decisions clearly	Identifies potential security vulnerabilities and privacy compliance risks
Fails to recognize security vulnerabilities or privacy risks	Takes appropriate action to address security and compliance concerns

Key Interview Questions:

• "How would you design the database schema for this application?"
• "What are the tradeoffs between normalization and denormalization in this case?"
• "How would you optimize query performance for this data model?"
• "How would you handle data migration and schema evolution?"

How Companies Evaluate Technical Skills

Companies look for evidence that candidates can:

1. Write Quality Code: Clean, maintainable, and efficient solutions
2. Solve Complex Problems: Break down problems systematically with optimal approaches
3. Design Scalable Systems: Create architectures that can evolve and scale
4. Design Efficient Databases: Create data models that optimize performance and usability
5. Apply Data Structures & Algorithms: Use optimal approaches to solve computational problems

Preparing for Technical Interviews

Story Preparation Framework

For each technical skill area, prepare stories that demonstrate:

• Context: What was the situation and why was it challenging?
• Action: What specific technical decisions did you make?
• Result: What was the outcome and what did you learn?
• Tradeoffs: What alternatives did you consider and why did you choose your approach?

Key Success Factors

• Ask Clarifying Questions: Always understand the problem before solving it
• Explain Your Thinking: Walk through your decision-making process
• Consider Tradeoffs: Discuss pros and cons of different approaches
• Handle Edge Cases: Think about error conditions and boundary cases
• Communicate Clearly: Adapt your explanation to your audience's technical level

Action Items

This section contains specific action items that readers can take to enhance their understanding or apply the concepts from this post:

• Audit Your Technical Skills: Review each of the 5 technical skill areas and identify 2-3 concerning behaviors you currently exhibit, then create a plan to develop the corresponding strength behaviors
• Prepare Story Bank: For each technical skill area, prepare 2-3 specific examples that demonstrate strength behaviors, using the Context-Action-Result framework with clear tradeoff discussions
• Practice Technical Communication: Record yourself explaining a technical decision you made, focusing on how you considered alternatives, evaluated tradeoffs, and communicated your reasoning to different audiences
• Mock Interview Preparation: Conduct practice sessions where you work through technical problems while explicitly demonstrating the strength behaviors from each skill area, especially asking clarifying questions and explaining your thought process

Implementation Notes:

• Each action item should be specific and measurable with clear deliverables
• Focus on the most impactful behaviors first (top rows in each table)
• Include expected outcomes: improved interview performance, clearer technical communication, better problem-solving approach
• Consider different skill levels: beginners should focus on basic functionality, intermediate on optimization, advanced on system design and stakeholder management
• Provide context: these behaviors directly correlate with interview success and job performance

Conclusion

Technical skills evaluation goes beyond coding ability—it assesses how you approach problems, make decisions, and communicate technical concepts. By understanding what companies look for and preparing stories that demonstrate these competencies, you can showcase your technical expertise effectively in interviews.

Remember: Companies want to see not just what you can build, but how you think, adapt, and collaborate to solve real-world technical challenges.

🤖 AI Metadata (Click to expand)

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