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The Discoverability Stack by Career Stage

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The Discoverability Stack by Career Stage

Student: Swati, you mentioned that the discoverability stack looks different depending on where someone is in their career. I've been assuming the advice is the same for everyone. Is it not?

Swati: It's absolutely not the same. A new grad fresh out of college has completely different assets and gaps than a senior engineer with 10 years of experience. And someone switching careers from a non-technical field has a different challenge entirely. The five layers are the same, but the weight of each layer shifts dramatically based on career stage.

I've brought in two founders who can speak to this from lived experience. Arun just went through the new grad job search - 14 consecutive rejections before landing at Apple at age 22. And Olena switched from financial analysis to software engineering at 26, getting rejected 11 times before landing at Microsoft. Their perspectives are not theoretical. They lived this.

The New Grad Stack

Arun: Hey. So let me tell you what the job search looks like when you have basically nothing. I graduated from Anna University with a 9.2 CGPA and top-500 Codeforces rating. On paper, I looked competitive. In practice, I got rejected 14 consecutive times - Atlassian, Flipkart twice, Amazon, Microsoft, Uber, and nine others.

Student: With a 9.2 GPA and competitive programming ranking? What went wrong?

Arun: My stack was completely unbalanced. I had competitive programming skills - which is basically just Layer 3 (GitHub/proof of work) - and nothing else. My resume was a one-page list of coursework and contest rankings. My LinkedIn didn't exist. I had no portfolio. My network was other students at my college. I was trying to get hired purely on algorithmic ability, and while that matters, it's not the whole picture.

Let me show you what the ideal new grad stack looks like versus what mine looked like:

graph TD
    subgraph "Ideal New Grad Stack"
        A1["Resume: 7<br/>Projects-led, clean format<br/>Coursework + internships"]
        B1["LinkedIn: 6<br/>Profile complete, skills listed<br/>Connected to alumni and recruiters"]
        C1["GitHub: 8<br/>3-5 well-documented projects<br/>Active contribution graph"]
        D1["Portfolio: 5<br/>Basic site with project case studies"]
        E1["Network: 5<br/>Professors, internship contacts<br/>Alumni, Discord communities"]
    end

    subgraph "My Stack at Graduation"
        A2["Resume: 3<br/>Academic-style CV<br/>No impact metrics"]
        B2["LinkedIn: 0<br/>Didn't have one"]
        C2["GitHub: 6<br/>Contest solutions only<br/>No real projects"]
        D2["Portfolio: 0<br/>Didn't have one"]
        E2["Network: 1<br/>Classmates only"]
    end

    style A1 fill:#51cf66,color:#fff
    style C1 fill:#51cf66,color:#fff
    style A2 fill:#ff6b6b,color:#fff
    style B2 fill:#ff6b6b,color:#fff
    style D2 fill:#ff6b6b,color:#fff
    style E2 fill:#ff6b6b,color:#fff

This diagram compares the ideal new grad discoverability stack with Arun's actual stack at graduation. The ideal stack has a resume scored 7 with projects and internships, LinkedIn at 6 with complete profile and recruiter connections, GitHub at 8 with well-documented projects, portfolio at 5 with project case studies, and network at 5 with professors and alumni contacts. Arun's actual stack had a resume at 3 with no metrics, LinkedIn at 0 because he didn't have one, GitHub at 6 with only contest solutions, portfolio at 0, and network at 1 with only classmates. Four of his five layers were critically weak.

Student: So for a new grad, which layers matter most?

Arun: For new grads, the priority order is:

1. GitHub and projects (Layer 3) - this is your primary evidence. You don't have 5 years of production experience. Your resume can't say "built a system handling 1M requests per day." What you CAN show is that you build things. Real projects - not tutorial follow-alongs - that demonstrate you can take a problem, design a solution, write the code, and deploy it. Two or three solid projects with detailed README files, deployed demos, and clean code are worth more than a 9.0 GPA.

2. Resume (Layer 1) - but it looks different from an experienced resume. A new grad resume leads with projects, not experience. Your coursework section matters. Your internships matter. Your personal projects matter. Impact metrics look different - instead of "reduced latency by 40% for a system handling 50M users," you might say "built a real-time chat application supporting 100 concurrent users using WebSockets and Redis."

3. LinkedIn (Layer 2) - more important than new grads think. I didn't have LinkedIn when I graduated, and I missed out on recruiter sourcing entirely. Recruiters absolutely search for new grad candidates on LinkedIn, especially from specific universities and with specific skills. Having a complete LinkedIn profile with your target role in the headline makes you findable.

4. Network (Layer 5) - this is where new grads struggle most. You don't have industry contacts. You haven't worked anywhere. Your network is professors, classmates, and maybe one or two people from an internship. But that's more than you think - professors can introduce you to alumni in industry. Classmates who graduated last year and are now at companies can refer you. Discord and online communities (like AlgoInsight's) connect you to people who've been through the exact same process.

5. Portfolio (Layer 4) - nice-to-have, not critical. For a new grad, your GitHub IS your portfolio. If you also have a personal website with project writeups, that's a bonus, but it's not the highest priority. Get the projects on GitHub first, worry about the portfolio website later.

Tip: Arun's New Grad Priority Stack:

1. 🔴 Build 2-3 real projects on GitHub with detailed READMEs (Modules 05, 07)
2. 🔴 Write a projects-first resume (Module 02)
3. 🟡 Create a complete LinkedIn profile with target role in headline (Module 04)
4. 🟡 Activate your thin network - professors, alumni, communities (Module 06)
5. 🟢 Portfolio website is optional but impressive (Module 05)

"I wish someone had told me this before my first 14 rejections. I was grinding LeetCode 8 hours a day and ignoring everything else. Algorithmic skill matters, but it's one input to a multi-variable equation." - Arun Selvam, Apple ICT3

The Career Switcher Stack

Olena: Hey. So I have a very different perspective from Arun because I came into tech from a completely different world. I studied Economics at Kyiv Polytechnic Institute. I worked as a financial analyst at PwC in Warsaw for four years. I learned to code at 26 from freeCodeCamp and YouTube.

When I started applying for engineering roles, my discoverability stack was... unusual.

Student: What do you mean by unusual?

Olena: I mean I had a strong resume - just not a tech resume. I had LinkedIn - but it screamed "financial analyst," not "software engineer." I had no GitHub. No portfolio. And my network was entirely in finance. Every layer needed to be either rebuilt or reframed for a completely different audience.

The career switcher's challenge isn't that their stack is empty - it's that their stack is optimized for the wrong audience. You have to simultaneously build new engineering credibility while reframing your existing non-tech experience as an asset rather than a liability.

Student: How did you handle that?

Olena: Here's what I learned across 11 rejections:

1. Portfolio (Layer 4) - this becomes your most important layer. For a career switcher, the portfolio is where you prove you can build things. Unlike an experienced engineer whose resume speaks for itself, or a new grad whose coursework provides structure, a career switcher needs an external artifact that says "I can do this work." A portfolio website with 3 to 4 projects - each with a problem statement, technical approach, screenshots, and deployed demo - is the single most valuable thing you can build.

2. Resume (Layer 1) - the reframing challenge. My resume had 4 years of financial analysis experience. The question was: is that a liability or an asset? The answer depends entirely on how you frame it. "Financial Analyst at PwC, 2015-2019" with no connection to engineering is a liability - it makes the recruiter wonder why you're applying for a tech role. "Financial Analyst at PwC - built automated reporting tools in Python, managed datasets of 1M+ records, designed dashboards used by 40+ stakeholders" is an asset - it shows that you were already doing engineering-adjacent work.

The career switcher resume needs to answer one question immediately: "Why should I take this person seriously as an engineer?" If the top third of your resume doesn't answer that, you've lost the recruiter.

3. GitHub (Layer 3) - proof that you can code. As a career switcher, you face a credibility gap. People will question whether you can really code. The fastest way to close that gap is to show them. GitHub projects - especially ones that solve real problems, not just tutorial exercises - are your evidence.

4. Network (Layer 5) - building from zero in a new industry. My entire network was in finance. I knew exactly zero engineers. Building a tech network from scratch felt impossible, but I did it through three channels: tech meetups in London (where I was living at the time), online communities, and one incredibly helpful Zalando engineer who responded to a cold LinkedIn message and eventually became my first tech reference. That one connection changed everything.

5. LinkedIn (Layer 2) - the identity transition. This is the trickiest part. Your LinkedIn currently says "Financial Analyst" but you want to be found by tech recruiters. You can't just delete four years of experience. Instead, you need to add your new identity on top - changing your headline to reflect your transition, adding your projects and new skills, and writing an About section that tells the story of your career change as a strength, not an apology.

graph LR
    subgraph "Career Switcher Priority"
        A["P0: Portfolio<br/>Prove you can build"] --> B["P1: Resume<br/>Reframe existing experience"]
        B --> C["P1: GitHub<br/>Show code evidence"]
        C --> D["P2: Network<br/>Build from scratch"]
        D --> E["P2: LinkedIn<br/>Transition your identity"]
    end

    style A fill:#ff6b6b,color:#fff
    style B fill:#ff6b6b,color:#fff
    style C fill:#ff6b6b,color:#fff
    style D fill:#fcc419,color:#000
    style E fill:#fcc419,color:#000

This diagram shows the career switcher priority order for the discoverability stack. The highest priority P0 is portfolio - proving you can build. P1 priorities are resume (reframing existing experience) and GitHub (showing code evidence). P2 priorities are network (building from scratch in a new industry) and LinkedIn (transitioning your professional identity from non-tech to tech).

Important: For career switchers: your non-tech experience is an asset, not a baggage.

Olena's financial analysis background didn't hurt her at Microsoft - it helped her. Product thinking, stakeholder management, data analysis, and communication skills are exactly what engineering teams need but often lack. The key is framing your previous career as complementary, not irrelevant.

"When I finally got to the interview at Microsoft, my interviewer asked about my transition from finance. I expected it to be a hurdle. Instead, she said, 'We have enough people who only know code. We don't have enough who understand the business.' My non-traditional background was the thing that differentiated me." - Olena Marchenko, Microsoft L60

The Experienced Engineer Stack

Student: What about someone with 5 to 10 years of experience who's already working in tech?

Swati: That's the most common profile we see, and the stack priorities are different again. When you have years of production experience, the equation shifts:

1. Resume (Layer 1) - your resume carries the most weight. Unlike a new grad whose resume is thin on experience, your resume has substance - companies, titles, projects, scale, impact. The challenge is not having enough content but curating it correctly. An experienced engineer's resume needs to show trajectory - growth in scope, complexity, and impact over time. And it needs to be tailored for each application, which is where AlgoInsight's AI Resume Builder saves you hours of work per application.

2. LinkedIn (Layer 2) - this becomes your primary discovery channel. At the experienced level, recruiters are actively sourcing. They search LinkedIn Recruiter for engineers with 5 to 10 years of experience at peer companies. If your LinkedIn is strong, opportunities find you. Many senior engineers I know haven't applied for a job in years - every role they've taken came through a LinkedIn InMail or a referral.

3. Network (Layer 5) - your most powerful asset. After 5+ years in the industry, you have something invaluable: colleagues, managers, and peers who've watched you work. These people can refer you, introduce you to hiring managers, and vouch for your abilities in ways that no document can. An experienced engineer who isn't leveraging their network is leaving their strongest channel unused.

4. GitHub (Layer 3) - less critical, more of a differentiator. At the senior level, nobody expects you to have a GitHub full of side projects. Your production work speaks for itself. But if you DO have a technical blog, open source contributions, or public projects, they're a differentiator - not a requirement. They say "this person cares about engineering beyond the job."

5. Portfolio (Layer 4) - optional but impressive. For most experienced engineers, the resume IS the portfolio. Your work history tells the story. A separate portfolio site is rare at this level and not expected. If you have one, great. If you don't, it's not a gap.

Student: So the priority order literally reverses between new grad and experienced engineer?

Swati: Almost exactly. A new grad's strongest play is showing what they can build (GitHub, projects). An experienced engineer's strongest play is showing what they've already built at scale (resume, LinkedIn). The career switcher sits in the middle - needing to prove they can build (like a new grad) while also reframing their existing experience (like an experienced engineer).

The Global Candidate Wrinkle

Student: What about international candidates? Does the stack change for someone outside the US?

Swati: Good question. I'll let Emeka address this fully in the modules on networking and running the search, but there are a few stack-level differences worth noting now.

For global candidates - engineers outside the US applying to US-based companies, or engineers in one country applying to companies in another - two additional factors matter:

Visa status visibility. Some LinkedIn profiles and resumes should explicitly mention visa status or authorization. "Authorized to work in the US" or "Require H-1B sponsorship" helps recruiters filter appropriately. Some candidates hide this, thinking it will hurt them. It might - but it saves both sides time, and companies that sponsor visas actively search for candidates who need sponsorship because those candidates tend to be highly committed.

Location-independent presence. Your GitHub, portfolio, and technical blog work the same regardless of geography. An engineer in Lagos with a strong GitHub profile is as discoverable as an engineer in San Francisco with the same profile. The digital layers of the stack are the great equalizer for global candidates - invest in them disproportionately.

Emeka went from Lagos to London to Amsterdam to Netflix in Los Gatos. His discoverability stack - particularly his Andela profile, his open source contributions, and his network through the Andela fellowship - was what made him visible to international recruiters. We'll cover his full story in Module 06.

Adjusting Your Audit by Career Stage

Student: So should I go back and redo my audit from the last topic with these career stage adjustments?

Swati: Not redo - reweight. Your scores don't change. A 3 on LinkedIn is still a 3 on LinkedIn regardless of your career stage. What changes is the priority of fixing each layer.

Go back to your audit and do this:

1. Identify your career stage (new grad, career switcher, 1-3 years experience, 4-7 years, 8+ years)
2. Use the priority table above to reorder your action plan
3. Set target scores that match your career stage - a new grad targeting a GitHub score of 8 is smart; an experienced engineer targeting a GitHub score of 4 is perfectly fine

Warning: Don't adopt the wrong career stage's priorities. I've seen experienced engineers with 8 years of experience spending months building GitHub projects because some advice blog told them to "build a portfolio." That's new grad advice applied to a senior engineer. At 8 years of experience, your time is better spent optimizing your LinkedIn and activating your network. Similarly, I've seen new grads obsessing over LinkedIn headline optimization while having zero projects on GitHub - that's experienced engineer advice applied to a new grad. Know your stage. Follow your stage's playbook.

Interview: Career Stage Awareness Shows Interview Maturity

One thing interviewers at the senior level evaluate is judgment - can you identify the right problem, prioritize correctly, and allocate resources where they'll have the most impact? Your discoverability stack audit, adjusted by career stage, is a perfect demonstration of that judgment.

In a behavioral interview: "I initially focused on perfecting my resume because I assumed it was the most important document. After doing a structured audit, I realized my LinkedIn was invisible to recruiters and my network was dormant - both of which had higher marginal returns than resume optimization. I reallocated my effort accordingly and saw a 4x improvement in response rates within three weeks." That's a story about prioritization, data-driven decision-making, and the ability to challenge your own assumptions.

Exercise: Starter: Identify your career stage from the categories in this lesson (new grad, career switcher, experienced engineer). Look at the priority table and compare it to how you've been spending your job search time. Are you working on the right layers for your stage?

Intermediate: Revisit your discoverability audit from Topic 02. Using the career-stage-adjusted priorities from this lesson, reorder your action plan. Does the priority shift change what you should work on first? Write down your revised top 3 action items.

Advanced: Find someone at a different career stage and compare stacks. If you're a new grad, find an experienced engineer and ask them what they prioritize. If you're experienced, talk to a recent grad. Understanding the other perspectives helps you see the full landscape - and you might discover that someone at a different stage could be a valuable addition to your network. This is networking in action, and we haven't even gotten to Module 06 yet.

Student: This changes everything for me. I've been treating the job search like everyone's path is the same. It's clearly not.

Swati: It's not. And knowing your stage - knowing which layers to invest in and which to deprioritize - is the difference between a focused, efficient search and months of effort in the wrong direction.

You now have a complete picture. You understand the five-layer discoverability stack. You've audited your current state. You know how to prioritize based on your career stage. Starting in the next module, we build the single most important document in your job search - the resume. We'll go deep. Brandon is going to tell you exactly what he looks for as an Amazon Bar Raiser who's reviewed over 300 resumes. Olena is going to show you how she reframed four years of financial analysis into an engineering resume. And I'm going to teach you the system for writing impact-driven bullets that make recruiters stop scrolling.

Let's go build it.

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