Technology Career Pathing

𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐚𝐬 𝐚 𝐥𝐢𝐧𝐞𝐚𝐫 𝐬𝐜𝐫𝐢𝐩𝐭 𝐢𝐬 𝐚 𝐛𝐮𝐠. It’s actually a 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 requiring high availability and fault tolerance. I realized that choosing a specialization in tech—be it Cloud Architecture, DevOps, or Full Stack—follows the same heuristics we use for 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝐬𝐢𝐠𝐧. Here is the breakdown of the "𝐂𝐚𝐫𝐞𝐞𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞" protocol: 1. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 (Know What You Like): Just as we analyze logs to understand system behavior, analyze your history. What topics do you advocate for during lunch? What GitHub repos do you star? This is your baseline telemetry. 2. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗣𝗿𝗼𝗳𝗶𝗹𝗶𝗻𝗴 (Heatmaps): In the sketch, I drew a heatmap matching "Good At" vs. "Like." In engineering terms, this is finding the sweet spot between 𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 (volume of work you can handle) and 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 (how much drag you feel doing it). 3. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗯𝘁 𝗔𝘃𝗼𝗶𝗱𝗮𝗻𝗰𝗲 (The 'Yuck' Stuff): This is crucial. Just because you are efficient at cleaning up messy legacy code doesn't mean you should specialize in it. If a task has high proficiency but low satisfaction, it represents future burnout—essentially, 𝒄𝒂𝒓𝒆𝒆𝒓 𝒕𝒆𝒄𝒉𝒏𝒊𝒄𝒂𝒍 𝒅𝒆𝒃𝒕. Deprecate these tasks early. 4. 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗔𝗣𝗜 𝗖𝗮𝗹𝗹𝘀 (Ask the Big Kids): Don't rely on cached data. Poll external nodes (Seniors, Principals). Ask about their daily stack, their leadership exposure, and their context switching overhead. 5. 𝗧𝗵𝗲 𝗖𝗔𝗣 𝗧𝗵𝗲𝗼𝗿𝗲𝗺 𝗼𝗳 𝗖𝗮𝗿𝗲𝗲𝗿𝘀 (Pick 2 & Look Closer): You usually have three metrics: 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗙𝘂𝗻, and 𝗣𝗮𝘆. It is rare to get strong consistency across all three immediately. Analyze your "Career Castles" (A vs. B) and decide which trade-off is acceptable for this specific epoch of your life. 6. 𝗥𝗼𝗹𝗹𝗶𝗻𝗴 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 (Start): Analysis paralysis is the enemy of uptime. If the metrics are close, deploy the instance that you are leaning toward. You can always rollback or re-architect later. Your career isn't a waterfall model; it's agile. Iterate often. Don't worry about a path not working out, you can always roll back :) #CareerPath #SystemDesign #SoftwareEngineering #TechCareers #Sketchnote

Building a Career Path Framework That Works I’ve learned that a well‑designed career ladder is far more than a “nice to have.” It’s a strategic tool for clarity, consistency, equity, and engagement. Here’s how I advise my clients to approach it: 1. Architecture first. Begin with a coherent job architecture: clearly defined job families, levels (Associate → Senior → Lead → Principal), and dual tracks (individual contributor and people management). Without clarity in job levels and scope, career pathing becomes ambiguous. 2. Eligibility criteria that mean something. Move beyond vague rules. Define for each level what “ready” looks like: impact, decision‑making, scope, leadership (of self or others). Then link promotions to demonstrated competencies and business need not just tenure. 3. Governance & alignment with pay. The career pathing program must be managed and owned by HR and business leadership, reviewed on a schedule, and aligned with your compensation structure and market competitive data. Too often organizations build the pathway and poorly integrate it with pay bands and performance assessment. Beware of job‑title inflation and other exceptions. 4. Keep it simple, socialize broadly, and iterate. Change doesn’t stick unless it’s understood. Use plain language, communicate broadly, equip managers to have career and compensation conversations, and treat the framework as a living ever-evolving system. If your organization is developing or refining a career pathing framework and you’d like to talk, I’d be glad to connect. Let’s ensure your investment drives transparency and talent mobility, not confusion. #CareerPathing #JobArchitecture #TotalRewards #Compensation #PayEquity #TalentDevelopment #HR #CompensationConsultant

Business Analysis is no longer a linear career path. It is a launchpad. As technology, AI, cloud, and cyber reshape every industry, Business Analysts have more opportunities than ever to evolve into roles that are both high-impact and future-ready. This visual captures that shift well: from technical paths like Cybersecurity Analyst, Cloud Engineer, AI/ML Engineer, and Data Analyst to non-technical but equally strategic roles like Product Manager, AI Consultant, Data Governance Specialist, and UX Researcher. What stands out most is this: The best career moves today are not just about job titles. They are about transferable skills. Business Analysts already know how to: - translate complexity into action - align stakeholders - uncover risk and opportunity - turn business needs into outcomes Those are the same capabilities that power success in secure AI, cyber resilience, cloud transformation, product strategy, and governance. From my perspective working across Cybersecurity, DevSecOps, Cloud, and AI Governance, I see a growing need for professionals who can bridge technical depth with business value. That bridge is where the future is being built. For anyone in Business Analysis wondering what comes next: Your next role may be closer than you think. The question is not whether you can pivot. The question is: Which direction aligns with your strengths, curiosity, and long-term impact? Which of these paths do you think will create the biggest opportunity over the next 3–5 years?

Picking the wrong tech career right now will cost you 12 months you can't get back. I scored 5 tech careers on what actually matters for career changers. Here's how they ranked: (Based on AI resistance, barrier to entry, proof-building potential, and long-term mobility) 5. Product Manager • Strong AI resistance and long-term ceiling • Trust-based role, you get in through formal experience and internal transfers • No portfolio equivalent exists to prove your skills to a stranger Hard cold entry for most career changers. 4. Cybersecurity Analyst • BLS projects 29% growth, nearly 10x the national average • Every new AI system creates more endpoints that need defending • Certification-heavy entry that takes real time to build Demand is real. Specialization required makes it a harder first step. 3. Software Engineer • BLS projects 50% growth • AI writes code but engineers own systems, debug failures, and make decisions that last years • Entry-level market is crowded and the technical ramp is steep Possible, but not the easiest first move without a technical foundation. 2. Cloud Engineer • Every AI tool needs infrastructure to run on • 98% of organizations say infrastructure is their bottleneck, not the AI itself • Hardest cold entry on this list with limited proof-building options early on Highest ceiling. Least accessible starting point. 1. Data Analyst • No degree required, no certifications needed to compete • Your background in healthcare, logistics, education, or retail is a direct advantage • Proof path is wide open from day one The reason this role holds up against AI has nothing to do with SQL or dashboards. It's about understanding what a business is actually asking, measuring what matters, and translating findings into decisions non-technical people can act on. That's where AI falls short every time. For most career changers, this is the role you can get into, prove yourself in, and grow from. Full breakdown of how to land your first data analyst role without going back to school is in my latest video. Link in the comments. PS: Which of these were you considering before reading this?

5 years ago, an “AI career” meant one thing: Machine Learning Engineer. That was it. Today, there are 15 distinct AI career paths. Each with different skills. Different pay ranges. Different long-term trajectories. And most people trying to enter AI don’t even realize half of these roles exist. Here’s what the top AI careers in 2026 actually look like 👇 - Machine Learning Engineer Build and deploy ML models that solve real-world problems at scale. - Computer Vision Engineer Enable machines to interpret and respond to visual data. - Deep Learning Engineer Focus on neural networks for vision, speech, and advanced pattern recognition. - Data Scientist Use statistics and modeling to turn complex data into business decisions. - NLP Engineer Build systems that understand, process, and generate human language. - AI Trainer Design and deliver AI education programs for teams and organizations. - AI Research Scientist Drive new discoveries. Publish research. Advance the field itself. - AI Product Manager Lead AI product strategy aligned with user needs and business goals. - AI Algorithm Specialist Design and optimize algorithms that power AI performance. - AI Data Analyst Extract insights from large datasets to improve AI systems. - AI Solution Architect Design end-to-end AI solutions integrated into enterprise systems. - AI System Integrator Ensure AI tools connect smoothly with existing workflows and platforms. - Robotics Engineer Develop intelligent automation systems across industries. - AI Consultant Guide organizations on AI strategy, adoption, and execution. - AI Business Analyst Identify where AI can drive efficiency and strategic advantage. AI is no longer just a technical niche. It now spans product, business, research, strategy, and operations. Every department in every company has an AI pathway inside it. The real question isn’t whether AI will shape your career. It’s which AI path you’re going to choose. Save this. Your career roadmap just expanded. Share this with anyone who thinks they’re “not technical enough” for AI. If you’re a founder or C-level leader and want help refining your LinkedIn identity - so your content attracts clients, not confusion - DM me #GrowMyLinkedIn. I’ll show you exactly how to position yourself for maximum visibility and inbound leads. Let’s make your presence impossible to ignore. Promised.

How to Change Careers into Tech? 𝗖𝗼𝗻𝗴𝗿𝗮𝘁𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀! 𝗬𝗼𝘂’𝘃𝗲 𝗺𝗮𝗱𝗲 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗮 𝗰𝗮𝗿𝗲𝗲𝗿 𝗰𝗵𝗮𝗻𝗴𝗲 𝗶𝗻𝘁𝗼 𝘁𝗲𝗰𝗵! Here is the system I have used for over 25 years. It helped me transition from healthcare to tech.  I’ve used this system with many others who are now working at Apple, Cisco, Google IBM, Microsoft, Accenture, Amazon, Deloitte, KPMG, Price Waterhouse Coopers (PwC), JP Morgan Chase, and many other prestigious organizations. 𝗦𝘁𝗲𝗽 𝟭: 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝘁𝗵𝗲 𝗷𝗼𝗯 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁   • Be very specific about the job you want.   • Although many jobs sound familiar, they require very different skillsets.  • To be successful, recognize you likely don’t know what you don’t know about the career you desire. 𝗦𝘁𝗲𝗽 𝟮: 𝗣𝘂𝘁 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿 𝘁𝗵𝗲 𝗣𝗹𝗮𝗻  • Network with successful experts in the role you desire   • Ask the experts about the skills you need for your career.  • Experts include people in the job you with titles like principal, distinguished, or director   • Don’t seek the advice of beginners or anyone who hasn’t had the job 𝗦𝘁𝗲𝗽 𝟯: 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝘁𝗵𝗲 𝘀𝗸𝗶𝗹𝗹𝘀   • Determine what skills you can learn on your own and what training you will need  • Get the required training 𝗦𝘁𝗲𝗽 𝟰: 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘁𝗵𝗲𝗶𝗿 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲  • Get relevant certifications to your career path after you learn the skills   • Certificates are for your brand and to help you get interviews   • If you only do the certifications, you won’t have the skills to be hired.  𝗦𝘁𝗲𝗽 𝟱: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗿𝗲𝘀𝘂𝗺𝗲 𝗮𝗻𝗱 𝗵𝗼𝗻𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗯𝗿𝗮𝗻𝗱  • Build your resume and LinkedIn profile  • Build, optimize, and protect your brand.   • Create content that builds your brand and shows you have the skills to be hired  • Engage with recruiters and hiring managers and strategically   • Apply for jobs strategically and with precision and care. 𝗦𝘁𝗲𝗽 𝟲: 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀   • Prepare for the interview  • Research the company (who they are, their accomplishments, and how you can add value to the organization).   • Research the hiring manager (background, career path, etc.).  • Practice your elevator pitch about who you are and what you can do for the employer  • Practice common behavioral questions and practice questions necessary for your career. 𝗦𝘁𝗲𝗽 𝟳: 𝗣𝗼𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄   • Stand out from the crowd. Send a thank you note of appreciation and use that as the last chance to sell yourself as the obvious choice for the role!  • Analyze the interview, what went well and what didn’t. Lessons learned are never a waste for the future.  • Determine any gaps and learn them. Fill them and share those new competencies with the world.  • Remember that anything worth having never comes easy. Repeat the process until you win! Make sure to follow Michael Gibbs for more #CareerChange #BreakIntoTech #TechCareers #JobSearchTips 

A simple guide for choosing the right career in tech: 1) Do not start with trends. Start with self-awareness. 2) Identify how your mind naturally works. - Love math, logic, research, and problem solving? AI and ML may suit you. - Curious, detail-oriented, and always thinking about risks? Cybersecurity could be the right fit. - Enjoy building systems, infrastructure, and reliability? Look into cloud engineering and DevOps. - Like business insights, patterns, numbers, and statistics? Data science is worth exploring. Remember that tech is not only coding. Fields like: * Product Management * UI/UX * QA * Business Analysis * Marketing Automation * Project Management also offer excellent career paths. Think long term. AI is growing rapidly. Cybersecurity will become increasingly urgent. Cloud remains one of the most stable domains. Data will be relevant in every industry. But in the end, the best field is not the one with the most hype. It is the one where your strengths compound over time.

Top 2026 Certification Pathways in Tech - https://lnkd.in/g-M5g3xf Quick note before we start. I shared a few pathway details last week that weren’t fully accurate, so consider this the corrected version. Appreciate everyone who helped tighten it up. Now to the bigger idea. Imagine two people starting their careers in the exact same place. Both begin as entry-level IT analysts. Same company. Same role. Same starting point. Five years later, their careers look nothing alike. One person followed the Azure infrastructure path and became a cloud architect designing enterprise systems. The other leaned into data and AI, built skills around Copilot and agentic workflows, and is now leading automation projects across multiple teams. Neither path was better. They were just different. That is what careers in tech look like now. In 2026, certifications are not a ladder you climb step by step. They are more like a skill tree with multiple directions you can grow into. Microsoft skilling paths let you branch into areas like Azure, Data, AI, Security, Power Platform, Copilot, and emerging agentic technologies. Every branch builds a different version of your career, and the direction you choose determines the kind of problems you solve, the teams you work with, and the opportunities you get access to. It is about intentionally stacking skills over time and aligning them with where you actually want to go, instead of drifting wherever the next project takes you. The people who win long term are not the ones who rush to grab every badge. They are the ones who choose a direction and build depth with purpose. If you are mapping out your next move, Microsoft Learn and Microsoft Cloud lay out the full skilling ecosystem in one place: Take time to explore the paths, pick a lane that fits your goals, and design a journey that makes sense for you. Careers are no longer one-size-fits-all. You get to build yours intentionally: https://lnkd.in/g-M5g3xf

For years, “future-proof your career” quietly became code for “learn to code.” But look at where the world actually is today. Yes, technology is dominating market value. No, that does NOT mean only technologists will dominate careers. Every major tech breakthrough has quietly created entire ecosystems of non-tech roles around it. AI didn’t just create AI engineers. It created: • Policy experts to regulate it • Ethicists to guide responsible use • Designers to humanize it • Educators to teach it • Sales teams to commercialize it • Lawyers to govern it • Psychologists to study its impact • Strategists to apply it across industries Technology is the engine. People, systems, and ideas are the steering wheel. Even within tech companies, some of the most influential roles today are in: → Product management → Business strategy → Operations → Marketing & storytelling → Customer success → Data interpretation (not just data science) → Partnerships & ecosystem building Industries are not becoming “tech only.” They are becoming “tech-enabled.” Healthcare needs clinicians who understand AI. Finance needs analysts who understand automation. Education needs teachers who can integrate digital tools without losing human connection. Creative fields need storytellers who can collaborate with machines without sounding like one. The future belongs to translators — people who can connect technology with real human problems. So if you’re choosing a career today, don’t ask: ❌ “Should I go into tech?” Ask instead: ✅ “What problems do I want to solve — and how will technology amplify my ability to solve them?” Because the winners of the next decade won’t just build technology. They’ll know what to DO with it. Careers don’t need to become technical. They need to become adaptable, interdisciplinary, and deeply human. And that’s a much more interesting future. #Careers #FutureOfWork #AI #Education #Leadership #Skills #CareerGrowth

Does tech seem intimidating and out of your reach? Think again. Your unique skills might be just what the industry needs. When I first considered tech, I counted myself out because I wasn't a coder. But then, I realized something crucial: tech isn't just about coding. It's about problem-solving, creativity, and leveraging diverse skills to drive innovation. I've seen many professionals from diverse backgrounds successfully transition into tech roles by leveraging their transferable skills. For instance, a friend with a background in project management excels as a product manager, while a former PhD student became a technical writer. ⚡ Let’s dispel the myths and embrace the possibilities. The truth is, tech is an ecosystem with numerous roles that require a variety of talents. Here are some actionable steps to carve your path in tech: ✅ Identify Your Transferable Skills: Whether it's project management, communication, analytical skills, and problem-solving, your existing skills can be a great fit for many tech roles. ✅ Explore roles beyond coding: Consider product management, technical writing, UX/UI design, sales engineering, data analyst, AI ethics, or customer success. ✅ Embrace emerging technologies: Stay curious about AI, data science, cybersecurity, and cloud computing. Get familiar with and use GPT tools. ✅ Start Small: Volunteer for tech-related projects or build a product. Real-world experience, even on a small scale, is valuable. Remember, tech thrives on diversity and innovation to solve problems and create value. You have a unique perspective to offer. Don't let self-doubt hold you back. With the right mindset and skills, you can thrive in tech. 🚀 #TransferableSkills #Technology #Innovation