Created on: 23 May 2025 ---- Last Update on: 30 May 2025

Technical and HR interviews – if you want to be successful in a recruitment process, you’ll need to go through both. Let's break down the typical stages of a tech hiring process.

Screening Call

Many companies initiate the recruitment process with a brief 15- to 30-minute introductory call. This crucial step allows both parties to assess mutual fit and ensure alignment, effectively preventing wasted time for everyone involved. Prioritize companies that employ this positive practice in recruitment over those that immediately proceed to technical challenges or interviews.

Do you know the company: Even if a recruiter contacts you directly (so you didn't apply for the job), always ensure you research the company's core business and target markets. This fundamental knowledge, though seemingly simple, can significantly influence your first impression. Remember, first impressions count.

Who are you: Be ready to articulate your professional background concisely. I typically ask candidates to describe their current role (key responsibilities, tech stack, team structure) and their future career aspirations. Keep it brief; this is a screening call, not a deep dive.

Language proficiency: For roles within multinational companies where English is the primary language of communication, be prepared to conduct the conversation in English.

HR Topics: Prepare to discuss your notice period (in some cases, you might be asked if it’s negotiable or not, and be honest about it, if you don’t know, just say it, don’t overpromise something you are not capable of). You’ll (or you should) approach salary & benefits expected.

• The sooner the better: Keep in mind that budgets are frequently flexible, varying based on the specific skills and value you can bring to the role and team. Aim to ascertain the potential flexibility or percentage by which the stated budget can shift. While a job posting might not include a salary range, don't dismiss an opportunity outright. Instead, get the compensation conversation out of the way as soon as possible to ensure alignment for both parties.
• Compare with the market, not with your peers: It is advisable to understand the business context and frame your compensation expectations based on demonstrable value and market data.
• Salary vs. Money: Contract, perks, equity, bonus, taxes…
• Highest Salary vs. Best Job: Remember, you have a career strategy, don’t you? ;)

Technical Interview

Show time!

• Engineering over syntax: If feasible, consider writing your code in a language different from the company's primary one. This encourages interviewers to focus on your implementation and problem-solving skills, rather than specific language 'tricks' or syntax nuances.

• Words over bits: Don't just deliver code; always provide a clear explanation of your decisions and implementation choices. This crucial step is often overlooked, but interviewers want to understand the "how" and "why" behind your solution, not just the outcome.

• Data over assumptions: Always ask for clarification on expectations. Don't hesitate to inquire what they're looking for: "code written in stone," quick fixes, branching, a runnable example—whatever it may be. Just ask.

Which type of questions to expect?

While specific questions will naturally vary across companies and even among different engineering teams within the same organization, some common themes and examples you might encounter include:

For ICs (Individual Contributors)

Please be aware that the questions listed below serve as illustrative examples of what may be asked during this step of the hiring process.

• Do you know and use [our product]? – Yes, they will ask you non-tech questions too! 😉
• One thing that could be improved. (If the previous answer is ‘Yes’)
• Why did you apply to [name of the company]?
• Share an experience where you needed to quickly adopt a new technology or tool to address an urgent challenge. What was your approach to learning and applying this new technology?
• Have you ever worked with cross-functional teams? Could you share an example of successfully bridging collaboration between different departments or teams?
• Walk me through your strategy for staying informed about emerging technologies and industry trends. Can you discuss a recent technology that excites you and how you think it could impact your work?
• What strategies do you employ when faced with multiple urgent tasks and limited time?
• How do you balance technical excellence with delivering business value? Can you share an example of where you had to make trade-offs between these aspects?
• Tell me about your motivation for pursuing this role and how your experience aligns with our team's needs.
• What's your approach to processing and acting on feedback, whether it's positive or negative? Can you give an example of how you implemented feedback to improve your performance?
• What drives your continuous development as a software engineer?
• Tell me about a time when you had to champion an unpopular but necessary decision with your team or stakeholders. How did you manage the situation?
• Could you share both a significant accomplishment you're particularly proud of and a situation you wish you had handled differently?
• Can you tell me something that you know you should improve?
• What do you expect to find on your team?
• What do you expect from your manager?

For Management

Please be aware that the questions listed below serve as illustrative examples of what may be asked during this step of the hiring process.

• Who’s [name of the candidate]?
• Do you know and use [our product]?
• One thing that could be improved? (If the past previous is ‘Yes’)
• Why did you apply to [name of the company]?
• Detail a bit about your experience as [name of the role].
• Who are your main stakeholders?
• Can you explain the team you managed, its size, and how it was structured?
• How do you manage priorities?
• How do you deal with tech debt?
• How do you deal with pressure and stress management?
• How do you manage your time management?
• How do you manage people management and people growth?
• What factors would you consider when deciding between two equally strong performers for a single promotion/increase opportunity?
• Have you managed a poor performer at some point in your career before? What did you do and how did it work?
• Describe your approach to managing a P1 incident that conflicts with a scheduled promotion discussion. How would you handle both responsibilities?
• What would be your step-by-step approach to addressing system conflicts, whether they're code-related or deployment issues?

In short, technical problem-solving interview questions try to understand:

1. Algorithm and data structure knowledge (DSA): Evaluate candidate’s understanding of fundamental algorithms and data structures, and their ability to apply them to solve problems.

• Practice on LeetCode and HackerRank: Focusing on data structures like arrays, linked lists, stacks, queues, trees, and graphs, as well as common algorithms such as sorting and searching, will lay a solid foundation for tackling more complex problems. You have other platforms such as GeeksforGeeks, CodeChef, HackerEarth at AtCoder.

• Understand time and space complexity: Learn to analyze the efficiency of your code by understanding time and space complexity, which is crucial for optimizing your solutions and making informed decisions during interviews.

• Develop problem-solving skills: Regularly solving coding problems helps develop analytical and logical thinking skills, which are essential for navigating the challenges of technical interviews.

• Review and refactor: After solving a problem, review your code for potential improvements. Refactoring can lead to more elegant and efficient solutions, showcasing your ability to improve upon your work.

Looking to delve deeper?

Online courses and specializations:

• Coursera:

  • "Algorithms Specialization" by Stanford University: A highly regarded series of courses that delve deep into algorithms.
  • "Data Structures and Algorithms Specialization" by University of California San Diego: It covers essential algorithmic techniques and data structures.
  • "Algorithms, Part I" and "Algorithms, Part II" by Princeton University: These courses on Coursera (also available via edX) use Java for implementations and are very thorough.

• edX:

  • "Algorithmic Design and Techniques" (IIT Bombay): A self-paced course that explores a wide range of algorithmic concepts.
  • "Data Structures and Algorithms" (GTx and UCSanDiegoX): Various programs and micro-credentials from top universities.

• Others:

  • Udemy: Many courses on DSA for various programming languages (Java, Python, C++, etc.).
  • freeCodeCamp: Offers comprehensive and free courses on data structures and algorithms, often with practical projects and a focus on JavaScript or Python. Their YouTube channel is also a great resource.
  • Khan Academy: Offers a beginner-friendly "Algorithms" course developed in partnership with Dartmouth College professors.
  • Log2Base2: Unique for its visual learning approach, using animations to explain complex DSA topics.

Books:

• "Introduction to Algorithms" by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein (CLRS): Often called the "Bible" of algorithms. It's comprehensive and covers theoretical aspects in depth. While dense, it's a “must-have” reference for serious learners.
• "Grokking Algorithms" by Aditya Bhargava: Excellent for beginners. It uses a very visual and intuitive approach to explain common algorithms with relatable examples.
• "Data Structures and Algorithms Made Easy" by Narasimha Karumanchi: A popular book for interview preparation, covering a wide array of problems and solutions.
• "The Algorithm Design Manual" by Steven S. Skiena: Combines theoretical explanations with a practical "War Stories" section and a catalog of algorithmic problems.
• "Cracking the Coding Interview" by Gayle Laakmann McDowell: While broader than just DSA, it has extensive sections on algorithms and data structures, specifically tailored for coding interviews at top tech companies.

Tips for Effective Learning:

• Choose a Programming Language: Pick one language (Python, Java, C++, JavaScript) and stick with it for your DSA journey. The concepts are transferable.
• Understand the "Why": Don't just memorize solutions. Understand why a particular data structure or algorithm is used for a specific problem and its time/space complexity.
• Implement from Scratch: Try implementing data structures and algorithms from scratch (e.g., a linked list, a binary search tree, merge sort) to solidify your understanding.
• Practice, Practice, Practice: Consistent problem-solving on platforms like LeetCode is the most effective way to master DSA. Start with easy problems and gradually move to medium and hard ones.
• Analyze Time and Space Complexity (Big O Notation): This is crucial for evaluating the efficiency of your solutions.
• Review and Iterate: Don't be afraid to revisit concepts and problems you found challenging.

2. System design: Assess candidates’ ability to design scalable and efficient systems based on given requirements.

Looking to delve deeper?

Online courses and specializations:

• Educative: "Grokking Modern System Design Interview for Engineers & Managers" is a highly recommended and comprehensive course, covering foundational to advanced topics and API design. They also offer crash courses.
• DesignGurus.io: "Grokking the System Design Interview" is a popular course known for its practical approach and detailed explanations of real-world design problems.
• ByteByteGo: Created by Alex Xu (author of "System Design Interview"), this platform offers structured learning paths and regular newsletters with system design puzzles and tips.
• Udemy: Look for courses like "Mastering the System Design Interview" by Frank Kane (ex-Amazon hiring manager) or "Pragmatic System Design" by Sandeep Kaul (Facebook engineer).
• Coursera: Offers specializations like "Software Design and Architecture" from the University of Alberta, which covers object-oriented design, design patterns, and software architecture.
• Exponent: Provides structured learning paths and is particularly useful for FAANG interview preparation.
• Arpit Bhayani's "System Design for Beginners": A self-paced course designed for early engineers, covering most topics from scratch.

Books:

• "Designing Data-Intensive Applications" by Martin Kleppmann: This is often cited as a “must-read” for anyone serious about system design. It delves into the principles behind reliable, scalable, and maintainable systems, covering databases, distributed systems, and design trade-offs.
• "System Design Interview – An Insider's Guide" (Vol. 1 & Vol. 2 by Alex Xu: These books (popularized by ByteByteGo) offer a practical approach to system design, walking through real interview scenarios and case studies.
• "The Art of Scalability" by Martin Abbott and Michael Fisher: Focuses on building scalable architectures and organizations, using real case studies from companies like eBay and Apple.
• **"Site Reliability Engineering (SRE)" by Google: Written by Google engineers, this book explores designing and operating large-scale systems with high reliability, covering availability, monitoring, and disaster recovery.
• "Clean Architecture" by Robert C. Martin (Uncle Bob): Part of his "Clean Code" series, this book offers insights into architectural principles.
• "Fundamentals of Software Architecture" by Mark Richards and Neal Ford: A comprehensive guide to software architecture, covering characteristics, patterns, and evolutionary architecture.

Blogs and online articles:

• High Scalability: This blog curates architecture lessons from top tech companies, often dissecting how major systems are designed.
• DesignGurus.io Blog: A self-study resource with a curated list of important system design interview questions and guides.
• ByteByteGo Newsletter: A weekly newsletter with system design puzzles and discussions.
• GeeksforGeeks: Offers numerous articles and a complete roadmap for learning system design, from basics to advanced concepts.
• DEV Community: You can find many articles comparing system design resources and providing detailed guides.

3. Debugging and performance optimization: Examine candidates’ skills in identifying and resolving issues, as well as optimizing code for better performance.

Debugging:

• Understand Your Tools: Every language/IDE has a debugger (e.g., Chrome DevTools, GDB, Visual Studio Debugger). Learn to set breakpoints, step through code, inspect variables, and view call stacks.
• Logging: Use print or logging statements strategically to trace execution flow and variable values.
• Reproduce the Bug: The first step is always to reliably reproduce the error.
• Isolate the Problem: Use binary search or divide-and-conquer to narrow down the faulty code section.
• Rubber Duck Debugging: Explain your code aloud to an inanimate object (or a person) – often helps identify the flaw.
• Version Control: Use Git to revert to previous working states.

Performance Optimization:

• Measure First (Profiling): Don't guess! Use profiling tools (e.g., perf, cProfile, browser performance tabs, Xcode Instruments) to identify actual bottlenecks (CPU, memory, I/O).
• Do Less Work:
  • Better Algorithms/Data Structures: Choose more efficient ones for your problem (e.g., a hash map vs. linear scan for lookups).
  • Avoid Redundant Work: Cache results, move computations outside loops, use lazy evaluation.
  • Optimize Data Access: Reduce network requests, optimize database queries, minimize DOM manipulations.
• Understand System Characteristics: Is your app CPU-bound, memory-bound, or I/O-bound? Optimize the right bottleneck.
• Asynchronous Operations/Parallelism: For long-running tasks, use asynchronous code or multithreading/multiprocessing (if applicable to your language/platform) to avoid blocking the main thread.
• Memory Management: Address memory leaks and optimize memory usage (e.g., reduce object creation).
• Trade-offs: Optimization often involves trade-offs (e.g., speed vs. memory, development time vs. runtime performance). Know when "good enough" is sufficient.

Key takeaway: Debugging is about finding why it's broken; optimization is about making it faster/more efficient. Both require understanding your tools, analytical thinking, and a systematic approach.

4. Code readability and maintainability: Analyze candidates’ approach to writing clean, readable, and maintainable code.

• Consistent Formatting:

  • Follow Style Guides: Adhere to established style guides for your language (e.g., PEP 8 for Python, Google Java Style Guide).
  • Linters/Formatters: Use tools like Prettier, Black, ESLint, or your IDE's built-in formatter to automate consistency.
  • Consistent Indentation, Spacing, Bracing: Small details matter.

• Meaningful Naming:

  • Variables, Functions, Classes: Use descriptive, clear names that indicate purpose or content (e.g., calculateTotalPrice instead of calc, userList instead of ul).
  • Avoid Abbreviations: Unless universally understood within your domain.
  • Be Consistent: If you use getUsers, don't then use Workspace_items.

• Clear Structure & Organization:

  • Modularize: Break down large functions/classes into smaller, single-responsibility units.
  • Logical Grouping: Keep related code together.
  • Consistent File Structure: Organize files and directories predictably.

• Effective Comments & Documentation:

  • Why, Not What: Explain why certain decisions were made, complex logic, or potential pitfalls, rather than just restating obvious code.
  • Docstrings/XML Comments: Use them for functions, classes, and modules to explain their purpose, arguments, and return values.
  • Keep them Updated: Outdated comments are worse than no comments.

• Simplicity & Clarity:

  • KISS (Keep It Simple, Stupid): Write the simplest code that solves the problem.
  • Avoid Over-Engineering: Don't build complex solutions for simple problems.
  • Limit Nesting: Reduce deeply nested if/else or loops.
  • Early Exit: Use guard clauses to handle error cases or edge conditions early, reducing indentation.
  • Single Responsibility Principle (SRP): Each function/class should do one thing and do it well.

• Avoid Duplication (DRY - Don't Repeat Yourself):

  • Refactor repeated code into functions or classes. Duplication makes changes harder and introduces potential for errors.

• Error Handling:

  • Graceful Errors: Handle errors explicitly and informatively.
  • Clear Error Messages: Make it easy to understand what went wrong.

Be aware that some companies conduct live coding sessions during the technical interview. Key concepts you should be familiar with include:

• Live coding exercises: Observe candidates’’ coding skills in real-time, including their problem-solving approach and coding style.
• Pair programming: Evaluate candidates’ ability to work collaboratively, communicate effectively, and solve problems as a team.
• Code reviews: Assess candidates' ability to write well-structured, efficient, and secure code through a peer review process.
• Technical challenges: Test candidates’ ability to apply their knowledge to solve complex technical challenges.

HR Interview

This step of the process is to understand two main components:

1. Soft-skills

Which soft-skills? The specific soft skills assessed depend directly on whether you are applying for an individual contributor role or a leadership position (e.g., Manager, Director, VP). Consequently, the interview's focus and depth will evolve substantially with the seniority of the role.

Regardless of the role's seniority, effective communication skills are paramount—without them, other competencies lose their impact. This stage often emphasizes your ability to communicate clearly, your resilience in difficult or frustrating situations (under pressure), and your openness to feedback.

2. ‘Cultural fit’

The concept of 'cultural fit' is frequently misunderstood by both companies and candidates. It should fundamentally mean bringing together diverse individuals who align with the organization's core principles and values. Therefore, ensure you are familiar with the company's stated values—these are typically published on their website—to assess your alignment.

Which type of questions to expect?

Please be aware that the questions listed below serve as illustrative examples of what may be asked during this step of the hiring process.

• Why do you think our company would be the next great step in your career?
• Tell me about a professional frustration.
• Tell me about the biggest challenge you had to face in your career so far.
• What do you expect from your manager? Which soft skills do you value in a manager?
• How do you react to feedback?

In short, behavioural interview questions try to understand:

• Handling workplace challenges: Explore how candidates navigate difficult situations and demonstrate problem-solving skills.
• Effective teamwork and collaboration: Discuss experiences in working within a team, showcasing communication and conflict resolution abilities.
• Adaptability and learning: Analyze how candidates have adapted to new challenges and what they have learned from these experiences.
• Leadership qualities: Examine instances where candidates have demonstrated leadership skills and the impact of their actions.

Overall – HR & Tech

This is not only about qualifications, not at all: The main goal is to check if your values and motivations are aligned with the company you’re interviewing to. Don’t prepare an interview. Prepare yourself to be interviewed: Instead of preparing questions in advance, memorizing “proper” answers, work on your verbal and body communication to expose your thoughts with the highest accuracy possible. 2-way Evaluation: Don’t forget you are evaluating too.

Don't overlook the opportunity to ask questions to your interviewers. Many candidates miss this crucial step. Throughout the hiring process, actively build documentation, gather insights, and strive to understand if the company, role, and culture truly align with your aspirations.

Best questions to ask the interviewers

About technical work:

• What are the significant technical challenges your team or company is currently navigating?
• Could you describe the most fulfilling/technically complex project that you've worked on here so far?
• What technical problems are currently demanding the most attention from your engineering team?
• How would you characterize your engineering culture?
• What's your current tech stack, and what's the story behind choosing it?
• How has your role evolved since you first joined the company?
• How does the engineering team balance resources between feature requests and engineering maintenance?
• What gets measured here, and what are your most critical product-related metrics?
• What resources are available for new hires to get up to speed on your product and processes? Is there robust documentation or clear specifications?
• Given my background, how do you see my expertise contributing to this team? What unique value could I bring?

About the culture:

• How is individual performance measured?
• What do you like about working here?
• What is the most frustrating part about working here?
• What is unique about working at this company that you have not experienced elsewhere?
• What does the company do to nurture and train its employees?

About team leadership/management style:

• What does success look like for your team/project?
• Can you tell me about a time you resolved an interpersonal conflict?
• How did you become a manager?
• How do your engineers know what to work on each day?
• What is your team's biggest challenge right now?
• How do you measure individual performance?
• How often are 1:1s conducted?
• What is the current team composition like?
• What opportunities are available to switch roles? How does this work?

Final Notes

Define a career strategy and focus on 3 key pillars:

• English
• Good practices
• Continuous learning

If you’re able to build a mindset around them, you’ll have a great career ahead.

Master skills, not tools!