Engineering Career

One of the biggest mistakes companies can make in the age of AI is overlooking early-career talent. Yes, AI is automating some of the more repetitive work that used to define many entry-level roles. However, that shouldn't lead us to pull back on investing in early-career talent. It should push us to rethink HOW we develop them. Why organizations need to lean into early-career talent: 1) Many early-career employees are more AI-native than anyone else in the organization. They often come in with fresh eyes and fewer assumptions about how work should be done. They're also curious, fast, and willing to get their hands dirty. It's something I've seen firsthand at ServiceNow in our finance rotators program, where early-career professionals rotate through different areas of finance. 2) When you lean into early-career talent, you create the conditions for reverse mentoring to happen naturally. These employees can help more experienced managers rethink workflows, challenge assumptions, and imagine what the job could look like tomorrow. The strongest organizations bring together early-career talent and experienced leaders. That kind of cross-pollination will matter enormously in a truly AI-native organization. The future belongs to companies that embrace AI—and empower the people most ready to reimagine how work gets done.

🔮 "AI and Big Data aren’t just trends — they’re the backbone of tomorrow’s economy." By 2030, the most valuable skillset won’t be just technical — it’ll be adaptable. Are you ready? According to the World Economic Forum’s Future of Jobs Report 2025, AI and Big Data skills are projected to see an 87% net increase in demand globally by 2030 India, with its rapidly expanding digital economy, is uniquely positioned to capitalize on this transformation. ▶️ 𝗜𝗻𝗱𝗶𝗮’𝘀 𝗧𝗲𝗰𝗵 𝗧𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆:  • The Indian tech industry is targeting $500 billion in revenue by 2030   • Demand for AI, Big Data, and Cybersecurity specialists is expected to grow by over 60%.  • Nearly 1 million young Indians enter the workforce every month — a demographic dividend that can become a global advantage if upskilled effectively. 📈 𝗧𝗼𝗽 𝗘𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗥𝗼𝗹𝗲𝘀 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮:  • Big Data Specialists: Critical to managing the explosion of data across industries.  • AI & Machine Learning Specialists: Driving automation, personalization, and innovation.  • Security Management Specialists: Safeguarding complex digital ecosystems. 🧠 𝗞𝗲𝘆 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘁𝗼 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗧𝗵𝗲𝗶𝗿 𝗖𝗮𝗿𝗲𝗲𝗿𝘀:  • Big Data Tools (Spark, Hadoop, Kafka)  • AI & ML Integration (MLOps, model deployment)  • Cloud Computing (AWS, Azure, GCP)  • Cybersecurity Awareness  • Analytical & Creative Thinking  • Technological Literacy & Agility 💡 𝗦𝗼𝗳𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 𝗠𝗮𝘁𝘁𝗲𝗿 𝗧𝗼𝗼:  • Resilience  • Flexibility  • Systems Thinking  • Collaboration across disciplines 🌍 Why This Matters: With 39% of core job skills expected to change by 2030  2, the future belongs to those who can adapt, learn, and lead in a tech-first world. Data Engineering isn’t just surviving — it’s evolving into one of the most strategic and high-impact roles of the next decade. 📘 Dive into the full World Economic Forum here: https://lnkd.in/gMExtKHr #Data #Engineering #AI #BigData

A collection of learnings from my 15-year Software Engineering career at companies like Meta, Pinterest, and Walmart. 1. To learn how to code, you must write code in an unstructured environment. Tutorials can help initially, but don't get stuck in tutorial hell: these engineers can't actually solve problems. 2. The only way to learn how to write good code is to write a bunch of terrible code first. It is fundamentally about the struggle. 3. Debugging is effectively playing a game of detective. Becoming an expert debugger in a large, complex codebase will make you extremely valuable to any company. 4. For software engineers, most of what you learn in school won't be relevant on the job. The biggest value of a university education is your network. Invest in getting to know students and faculty. Don't worry too much about grades. 5. Networking is about building long-term relationships built on trust and value. Give more than you take and your network will grow rapidly. Remember this phrase: "Your net worth is your network." 6. Everyone in tech faces imposter syndrome. Consider imposter syndrome as an opportunity to learn from people who are further along. Actively seek out feedback and talk to people. 7. Tech interviews are immensely broken and your interviews will probably differ from your job. View interviews as a learning opportunity where you get to meet some other cool, smart people. 8. Realize that the average person will spend < 10 seconds scanning your resume. No one is as interested in you as you, so you need to keep things short. Your resume should be 1 page long. 9. Feedback is the secret to rapid career growth. Make it easy for others to give feedback by introspecting and asking for specific parts of your behavior. A lazy “Do you have any feedback for me?” will often be met with a similarly lazy “Nope, you’re doing great!” 10. If you're not sure what company to join, go to a larger, well-respected company (FAANG) as your first job. Junior engineers benefit from the consistency and stability of Big Tech. 11. Onboarding is a magical time when you get a free pass to ask as many questions as possible, request people's time, and build foundational relationships. Work with a sense of urgency when you're new to a company. 12. The relationship with your manager is the most important relationship you'll have in the workplace. You should proactively drive meetings and feedback with your manager; don't wait for them. 13. Getting promoted as an engineer is not just about skill or output. You also need scope and trust. Most promotions are deliberately planned months in advance. If a promotion is important for you, bring it up with your manager well in advance. 14. Most engineers don't negotiate their offers, but they should. The most important tool for negotiation is leverage. This means competing offers. I put this all together in a 1.5-hour video here: https://lnkd.in/gAH4Q2pD

After appearing in 20+ interviews, I have figured out how much Coding skills exactly required for each of Data Scientist, ML Engineer and AI Engineer roles. Do not get confused — these are separate roles and requires different coding expertise. 1. Data Scientists (product/analytics roles): - SQL is non-negotiable. - Python scripting + Pandas/NumPy are your daily tools. - DSA easy-medium (Leetcode enough!) Interviews often include: • SQL case studies • Data wrangling challenges (I got it many times!) • One Leetcode easy/medium — often string or array manipulation 2. Machine Learning Engineers: - You're expected to think like an engineer and a data scientist. - Writing production-quality code matters. - You’ll be tested on coding patterns, not just scikit-learn usage. Interview Expectations: • Leetcode medium (sometimes hard - but less chance) • Algorithmic thinking (e.g., optimizing training loop performance) • ML system design (batch vs streaming, deployment strategies) 3. AI Engineers / Applied Scientists: - Especially in LLM/Deep Learning-focused teams, system design + performance-aware coding is key. In this role you’ll deal with: - Large-scale data - GPU memory optimization - Custom training loops - Vector search, graph traversal, and more Coding rounds often include: • DSA-heavy problems (graphs, trees, recursion, DP) • Code optimization tasks • Python internals, multi-threading + processing, OOPs, memory & complexity analysis Coding is totally non-negotiable in every case. No matter how much theoretical knowledge you hold, if you can't solve live coding in interview you are straightaway rejected. For system coding I have started writing ML system design articles, feel free to check-out. [Link in comment]

Ever promoted someone... by encouraging them to leave their job? I have. And it was the right thing to do. One of my former EAs was next-level brilliant. Fast, precise, calm under pressure. The kind of person who finishes before you’ve even finished explaining the task. She was every exec’s dream. Which is exactly why I knew we couldn’t keep her in that role for long. She had outgrown it. The job stopped challenging her. She was coasting and not in a lazy way, in a this-is-too-easy-for-me way. Now here’s the dilemma: Keeping her in that role made my life easier. But keeping her in place was stopping her from moving forward. So I did something that most leaders avoid: I initiated the conversation that could have led her to quit. Instead, we shifted her into a new team. Different challenges. New growth curve. And today, she’s thriving. Because let’s be honest: 1. High-performers don’t stick around for long if they feel boxed in. 2. Growth isn’t just a perk, it’s oxygen for top talent. 3. And roles that don’t evolve become cages even if the view is nice. According to McKinsey, lack of development opportunities is the #1 reason people quit. And LinkedIn data shows that companies with strong internal mobility retain employees 2x longer. If you want a company that scales, build one that lets people grow. Even if it means reworking job descriptions. Even if it makes your own day a bit harder. Short-term inconvenience is a small price to pay for long-term loyalty, innovation, and momentum. So ask yourself Are you building a team that thrives? Or a team that stays useful? Because those aren’t always the same thing.

Being early in your career doesn’t mean you’re any less valuable. You might not have 10 years of experience. But you do have ideas. Energy. A fresh perspective. And guess what? That matters. You see things differently. You ask questions that challenge the status quo. You notice problems others have stopped seeing. But too often, early-career talent gets overlooked. Dismissed. Told to “wait their turn.” Here’s the truth: Experience is earned. But value? You bring that from day one 💖 If you’re in the room - you deserve to be heard. Not just seen. And if you’re leading a team? Listen to the people who are just getting started. Because one day, they’ll be the ones leading you 🫶

⚡ On-Site Engineer Diaries: Earning Comes From Learning ⚡ One thing the field teaches you fast: Every earning—knowledge, experience, money, or recognition—starts with learning. As a commissioning engineer, the biggest advantage of on-site work is exposure. Every visit to cement, steel, oil & gas, or energy plants gives real-time clarity that no manual can provide. Because on site, technology becomes tangible — you see every parameter, load, and application come alive in front of you. A few things I’ve learned along the way: 🔸 Understand the application — Know what your motor is driving and why the VFD is chosen for that duty. 🔸 Observe the process — Speed profiles, torque demand, inertia, and load fluctuations vary across industries. 🔸 Connect the dots — See how drives improve efficiency, stability, and energy usage in actual operation. 🔸 Ask without hesitation — Operators, maintenance teams, and process engineers hold insights no datasheet can capture. 🔸 Use every visit to upgrade yourself — Site is where electrical, mechanical, and process engineering meet in reality. Opportunities don’t repeat. Learning doesn’t wait. And sharing knowledge multiplies it. Keeping the journey simple: Learn. Apply. Grow. Repeat.⚡ #OnSiteEngineerDiaries #CommissioningEngineer #MotorsAndDrives #LearningEveryday

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

We love to tell business leaders, “Disrupt or Be Disrupted” and “Adapt or Fail,” but it’s hard to hear that applies to our careers, too. Part of every business, product, AND career cycle is the end. According to the Harvard Business Review, skills have a half-life of less than 5 years. Spending 10 years delivering the same products, code, and models or practicing the same leadership style is risky. We can’t imagine a time when capabilities that drove 10 years of promotions and job offers can’t keep the momentum going. It’s incomprehensible to be out of work for months after a layoff until it happens. Best-in-class products command best-in-class prices until competitors deliver a substitute for less. It’s the same with best-in-class talent. Margins and salaries are always driven down in the long run. We should maintain our skills annually, modernize them to deliver higher-demand products/outcomes every 5 years, and reskill for a career transition every 10-15 years. A growth mindset must be updated to include, “The skills and capabilities that got us this far aren’t the same as the ones that will take us to where we must go next.” We must manage our career growth by profiting from disruptions and continuous change. Good news: it’s pretty easy. Just keep finding new challenges and problems you’re passionate about solving. Bad news: it takes continuous change, learning, improvement, discomfort, and discipline. Passion is critical. Align your career with your personality and interests so you’re engaged enough to do the hard parts.

The "GIS Career Ladder" has collapsed. For 20 years, the path was linear. GIS Technician to GIS Analyst to GIS Manager. You waited your turn. You accumulated years of experience. You hoped a spot opened up above you. That model is officially obsolete. The modern geospatial stack has shattered the single ladder into 7 distinct, high-growth verticals. The "Jack of All Trades" who tries to be a database admin, a web developer, a cartographer, and an analyst is struggling. Why? The stack is too deep. You can’t master it all. The winners in 2026 are choosing a specific lane and accelerating. Here are the 7 new career paths replacing the "Analyst": 1. The Plumber (Geospatial Data Engineer) Focus: Cloud-native formats, orchestration, medallion architecture. 2. The Bridge (Analytics Engineer) Focus: Sitting between raw data and business logic. Making queries lightning fast. 3. The Strategist (Spatial Analyst) Focus: Stakeholder needs. Translating business problems into visual insights. 4. The Predictor (Spatial Data Scientist) Focus: Python, PySAL, clustering, and forecasting future trends. 5. The Builder (Geospatial Developer) Focus: Full-stack apps. React and Mapbox/Leaflet (not dashboards). 6. The Architect (Cloud Architect) Focus: Governance, security, and designing the system that holds it all together. 7. The Scaler (ML Engineer) Focus: Taking the Data Scientist’s models and keeping them alive in production. Stop trying to climb the old ladder. Pick a vertical. Master that specific part of the stack. That is how you increase your value (and your salary) this year. I have a breakdown on how to position yourself for these new roles. Comment "CAREER" below and I'll DM it to you. 🌎 I'm Matt Forrest and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 12k+ others learning from my daily newsletter → forrest.nyc