Preparing for senior software engineering interviews at big tech is not just about solving LeetCode problems.
At senior levels, the interview loop usually tests a broader set of skills:
- Can you solve ambiguous engineering problems?
- Can you design systems with reasonable tradeoffs?
- Can you communicate clearly under pressure?
- Can you explain past work with senior-level judgment?
- Can you show technical depth without overcomplicating the answer?
I used a mix of AI tools, interview prep platforms, design tools, and personal tracking systems to prepare across these areas.
Tools I used
| Category | Tool | URL | How I used it |
|---|---|---|---|
| General AI assistant | ChatGPT | https://chatgpt.com | Breaking down concepts, simulating interviewers, reviewing answers |
| AI writing / reasoning | Claude | https://claude.ai | Refining behavioral stories and system design explanations |
| Coding practice | LeetCode | https://leetcode.com | Core DSA practice and timed coding drills |
| Coding explanations | NeetCode | https://neetcode.io | Pattern-based review for common coding problems |
| Architecture sketching | Excalidraw | https://excalidraw.com | Quick system design diagrams |
| Product / UX thinking | Figma | https://figma.com | Wireframing product flows and interface-heavy design discussions |
| Notes and tracking | Notion | https://notion.so | Tracking problems, weak areas, stories, and interview learnings |
| Mock interviews | Skillio | https://tryskillio.com | Practicing realistic role-specific interviews and getting structured feedback |
My overall approach
I split preparation into five tracks:
- Coding interviews
- System design
- Behavioral interviews
- Resume and project deep dives
- Mock interview practice
This mattered because each track required a different preparation style.
Coding needed repetition and pattern recognition. System design needed structured thinking. Behavioral prep needed storytelling. Mock interviews needed pressure and feedback. Resume deep dives needed clarity on the actual impact of my work.
A single tool was not enough. The stack mattered because each tool solved a different problem.
1. Coding interviews: pattern recognition over random grinding
For coding, I used LeetCode and NeetCode as the core stack.
My goal was not to solve the maximum number of problems. It was to get comfortable with the most common patterns:
- Arrays and hash maps
- Two pointers
- Sliding window
- Binary search
- Trees and graphs
- BFS / DFS
- Dynamic programming
- Heaps
- Intervals
- Backtracking
The biggest improvement came when I stopped treating every problem as new.
Instead, after solving a problem, I would write down:
- What pattern was used?
- What was the key insight?
- What mistake did I make?
- Would I recognize this pattern again in a different problem?
I used ChatGPT mostly after solving or getting stuck. I avoided asking it for the final answer immediately. Instead, I used prompts like:
Give me a hint without revealing the full solution.
Or:
Explain the pattern behind this problem and show me how to recognize similar problems.
That made the AI useful without turning it into a shortcut.
For senior engineer interviews, I also practiced explaining the solution out loud. A correct solution is not enough if the interviewer cannot follow your reasoning.
2. System design: practicing structure, not memorizing architectures
System design prep was the most important part of my senior SWE preparation.
At senior levels, system design interviews are usually less about whether you know the perfect architecture and more about whether you can reason through ambiguity.
I used Excalidraw for quick architecture sketches. I preferred it over heavier tools because it forced me to stay rough and fast.
My system design flow usually looked like this:
- Clarify requirements
- Define functional and non-functional constraints
- Estimate scale if needed
- Sketch the high-level architecture
- Deep dive into one or two critical components
- Discuss bottlenecks
- Discuss tradeoffs
- Close with failure modes and future improvements
I used AI tools to stress-test my designs.
For example, after designing a feed system, I would ask:
Act like a senior staff engineer interviewing me. What are the biggest weaknesses in this design?
Or:
What follow-up questions would a big tech interviewer ask on this architecture?
This was useful because system design prep often creates a false sense of confidence. You can read a polished architecture and feel like you understand it, but the real test is whether you can defend your choices live.
I also used Figma occasionally when the design problem involved user-facing flows, dashboards, internal tools, or configuration-heavy products. It helped me think through the interface, not just the backend.
That said, Figma was secondary. For most system design interviews, Excalidraw was enough.
3. Behavioral interviews: building a reusable story bank
Behavioral prep is easy to underestimate.
For senior engineers, behavioral interviews are not just about being pleasant. They test signals like:
- Ownership
- Conflict resolution
- Technical leadership
- Mentorship
- Decision-making under ambiguity
- Cross-functional collaboration
- Handling failure
- Raising engineering standards
I created a story bank in Notion.
Each story had this structure:
| Field | What I wrote |
|---|---|
| Situation | What was happening? |
| Problem | Why did it matter? |
| Role | What was I responsible for? |
| Action | What did I actually do? |
| Tradeoffs | What alternatives did I consider? |
| Result | What changed because of my work? |
| Reflection | What would I do differently now? |
I did not want robotic STAR answers. The goal was not to memorize scripts. The goal was to have well-structured raw material.
Then I used AI to improve the stories.
Useful prompts included:
Make this story sound more senior without exaggerating my role.
What leadership signals does this answer show?
What follow-up questions would an interviewer ask?
Rewrite this answer to be tighter and more outcome-driven.
The main lesson: behavioral prep gets much better when you treat it like product positioning. You are not inventing stories. You are packaging real work clearly.
4. Resume and project deep dives: preparing for scrutiny
For senior software roles, interviewers often go deep into past projects.
They may ask:
- Why did you choose this architecture?
- What was the hardest technical problem?
- What would break at 10x scale?
- What did you personally own?
- How did you measure success?
- What tradeoffs did you make?
- What would you do differently?
I picked 4-5 major projects from my resume and created a deep-dive sheet for each.
The template looked like this:
| Area | Notes |
|---|---|
| Context | What was the business or engineering problem? |
| My role | What did I directly own? |
| Architecture | What was the technical design? |
| Scale | Users, requests, data size, latency, cost, or team size |
| Tradeoffs | What options did we reject and why? |
| Failure modes | What could go wrong? |
| Impact | Metrics, reliability improvements, revenue, cost, speed, or adoption |
| Lessons | What I learned as an engineer |
This helped me avoid vague answers like “I worked on improving performance.”
Instead, I could say what changed, why it mattered, and how I made decisions.
This is especially important at senior levels because interviewers are looking for judgment, not just implementation ability.
5. Mock interviews: where the gaps became obvious
The biggest difference between studying and interviewing is pressure.
You may understand a concept when reading it, but struggle when you need to explain it live, structure an answer, and respond to follow-ups.
That is where mock interviews helped.
I used Skillio for practicing realistic interviews and getting feedback. The useful part was having a more interview-like environment instead of just chatting with a generic AI assistant.
For me, the value of mock interviews was in identifying issues like:
- Rambling answers
- Weak opening structure
- Missing clarifying questions
- Jumping into implementation too early
- Not explaining tradeoffs clearly
- Giving behavioral answers without enough impact
- Sounding less senior than the actual experience justified
This is the kind of feedback that is hard to get from solo prep.
I also used ChatGPT for lighter interview simulation, especially for quick drills. For example:
Interview me for a senior backend engineer role. Focus on distributed systems and ask one question at a time.
But for more structured interview prep, I preferred using a dedicated mock interview flow.
How I used AI without becoming dependent on it
AI was useful throughout the process, but I tried not to let it replace thinking.
The best use cases were:
| Use case | Good AI usage | Bad AI usage |
|---|---|---|
| Coding | Hints, pattern explanation, complexity review | Asking for full solutions immediately |
| System design | Stress-testing tradeoffs | Memorizing generated architectures |
| Behavioral | Tightening stories | Inventing fake experience |
| Resume prep | Finding weak spots | Over-polishing until it sounds unnatural |
| Mock interviews | Simulating pressure | Avoiding real practice |
The rule I followed was simple:
Use AI to expose gaps, not hide them.
If I could not explain the answer without AI, I was not prepared.
My weekly preparation structure
A typical week looked something like this:
| Track | Frequency | Focus |
|---|---|---|
| Coding | 4-5 days/week | Pattern practice and timed problems |
| System design | 2-3 days/week | One full design problem plus review |
| Behavioral | 2 days/week | Story bank refinement and live answers |
| Resume deep dives | 1-2 days/week | Project-level technical detail |
| Mock interviews | 1-2 sessions/week | Pressure testing and feedback |
This was not perfectly followed every week, but it gave structure to the preparation.
The most important thing was balancing depth and repetition.
Only doing coding made the prep too narrow. Only reading system design made it too passive. Only writing behavioral stories made it too theoretical.
The combination mattered.
What I would do differently next time
If I were doing this again, I would start mock interviews earlier.
I spent too much time preparing in isolation before testing myself in interview-like conditions. That delayed useful feedback.
I would also create my behavioral story bank earlier. Senior interviews often use past experience as the backbone of the conversation, so having strong stories helps across multiple rounds.
Finally, I would track mistakes more rigorously.
Not just:
I got this problem wrong.
But:
I failed because I did not identify the sliding window pattern.
Or:
My system design answer lacked a clear bottleneck analysis.
The more specific the mistake, the easier it is to fix.
Final thoughts
Interview prep for senior software engineering roles is not just about knowledge.
It is about performance.
You need to retrieve information quickly, structure ambiguous problems, explain tradeoffs, and communicate in a way that gives interviewers confidence.
The tools helped, but the system mattered more:
- Use coding platforms for repetition.
- Use AI tools for feedback and stress testing.
- Use design tools to make architecture visible.
- Use notes to track patterns and stories.
- Use mock interviews to practice under pressure.
That combination made my preparation much more deliberate and much less random.
Top comments (1)
It's good to see a prep stack that recognizes the limits of just grinding LeetCode for senior roles. The structured story bank for behavioral interviews is a solid idea, but I'm getting burned out on story prep. I'm focusing on coding and system design and ended up using PracHub for those mocks. Their bank matches the follow-up questions I encountered in my tech screens. It's like a hidden gem next to the usual LC grind.