Tech Skills for Future Jobs

By 2030, these 11 abilities will decide who gets hired Most don’t show up on resumes yet. The World Economic Forum just revealed the top skills for 2030 in the Future of Jobs Report 2025. And it’s a wake-up call. Today's celebrated tech skills? AI will do those better by 2026. Those certifications? Outdated in 18 months. But here's the good news: The skills that matter most in 2030? Technology can't replace them. Start mastering these skills to stay relevant and be recognized: 1. AI and Big Data 🤖 ❌ Passively watch AI replace jobs ✅ Make AI your competitive edge → Use AI to automate weekly reports → Build self-updating dashboards and summaries 2. Analytical Thinking 🧠 ❌ Drown in opinions and noise ✅ Let data drive key decisions → Identify root causes before reacting → Monitor metrics that reveal blind spots 3. Resilience, Flexibility and Agility 🐆 ❌ Break down under shifting priorities ✅ Adapt fast and lead through change → Stay steady during messy execution → Pause, breathe, ask: “What’s the next best move now?” 4. Motivation and Self-Awareness 👤 ❌ Burn out chasing urgency ✅ Work in sync with your energy → Track your energy every 3 hours for a week → Schedule focus work when your mind feels sharp 5. Curiosity and Lifelong Learning 🔍 ❌ Stick to your job description ✅ Learn a complementary skill to your role → If you're in marketing, study basic product design → If you're in finance, explore storytelling with data 6. Leadership and Social Influence 🌟 ❌ Rely on your title for respect ✅ Build trust by how you think, speak and act → Explain why you made a tough call, not just what you decided → Share a client insight that helped your team level up 7. Technological Literacy 💻 ❌ Run to the IT helpdesk for every issue ✅ Build and adapt your own stack → Automate one repetitive workflow today using AI → Use familiar tools more efficiently (Excel, Slack) 8. Systems Thinking 🔧 ❌ React to broken processes ✅ Design workflows that scale → Improve one repeated but inefficient process this week → Ask: “Can this run without me?” 9. Empathy and Active Listening 🎧 ❌ Talk to be heard ✅ Listen to support, inspire and lead → Listen without needing to speak more in 1:1s → Decode what’s really being said 10. Creative Thinking 🎨 ❌ Wait for inspiration ✅ Build innovation into routine → Ask: “What’s another way to solve this?” → Try a small change to test a new idea 11. Talent Management 👥 ❌ Try to do it all ✅ Delegate and develop future leaders → List 3 tasks to delegate now → Improve hiring processes to onboard the right talent 💡 It’s not about doing more. It’s about evolving how you think, lead, and grow. Because the future expects you to. Which one are you focusing on this month? -- ♻ Share this with someone you’d want on your 2030 team. ➕ Follow me (Meera Remani) for future-ready leadership strategies. 🔔 My best insights for transforming your leadership career? Join my exclusive email list. Link below.

When I first stepped into the world of cybersecurity, I was completely lost. I didn’t know where to start, what to learn first, or how people even got into this field. All I knew was—I wanted to be a part of this world where people protect, investigate, and defend against digital threats. 💻⚡ At first, everything looked complicated: hacking, tools, reports, and those mysterious terms like “VAPT” and “SOC.” But slowly, I realized that becoming a cybersecurity professional isn’t about learning everything at once—it’s about building layer by layer. So here’s how the journey begins 👇 📍 Step 1: Build your base Understand the fundamentals — Computer basics, Networking, Linux, Windows, and a bit of Programming. This is your foundation. Without it, cybersecurity concepts won’t make sense. 📍 Step 2: Explore the world of security Learn about Web Security, System Security, Network Security, Cryptography, and Cybersecurity Fundamentals. Then dive deeper into areas like VAPT, Incident Response, Digital Forensics, and Cloud Security. 📍 Step 3: Play and practice This is where learning gets fun! Platforms like TryHackMe, HackTheBox, PortSwigger Academy, OverTheWire, VulnHub, and LetsDefend are your playgrounds. Each challenge you solve teaches you real-world skills. 📍 Step 4: Find your direction You can become a Security Analyst, SOC Technician, Penetration Tester, Threat Intelligence Analyst, or even a Cloud Security Associate ☁️ Each path has its own tools, techniques, and challenges. 📍 Step 5: Prepare for your career Start building projects, upload your reports to GitHub, and prepare at least three pentest reports. Add certifications like CompTIA Security+, CEH, or OSCP. And don’t forget to network on LinkedIn — it opens doors you didn’t even know existed. 🤝 🔥 My advice? Start small, stay consistent, and document everything you learn. Cybersecurity isn’t just about hacking—it’s about protecting, analyzing, and defending. 💪 So if you’re someone who’s confused, just like I was—this roadmap is your compass. Let’s build the next generation of ethical hackers and defenders together. 💣 If you’d like resume guidance, just DM me your “RESUME.” And for more such content, follow my channel: 👉 https://lnkd.in/gGAnR_UF #CyberSecurity #EthicalHacking #InfoSec #TryHackMe #HackTheBox #VAPT #PenTesting #DigitalForensics #SOC #IncidentResponse #BlueTeam #RedTeam #BugBounty #NetworkSecurity #CloudSecurity #Linux #CompTIA #CEH #OSCP #SecurityAnalyst #CyberCareer #CybersecurityCommunity #CyberAwareness #TechCareers #CyberInternship #CyberLearning #InfosecJourney

I've trained 10,000s of engineers - from developers in Brazil to Fortune100 teams in US to government employees in UK. These 8 𝘁𝗿𝗮𝗶𝘁𝘀 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝗴𝗼𝗼𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗲𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗼𝗻𝗲𝘀: Here's the thing - AI is reshaping how we build and deploy software. But it's not replacing the engineers who have these skills. If you're a manager, you need to know what truly matters. And if you're an engineer, these are your career insurance. 1) 𝗔𝗰𝘁𝗶𝗼𝗻 𝗕𝗶𝗮𝘀 ↳ Most engineers wait for perfect requirements. ↳ Top performers release fast and iterate based on actual user feedback. 2) 𝗧𝗶𝗺𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗠𝗮𝘀𝘁𝗲𝗿 ↳ They don't just manage their calendar - they protect their focus. ↳ They say no to unnecessary meetings and protect deep work time. 3) 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 ↳ They translate technical complexity into clear language. ↳ No jargon. No waffle. Just clarity that aligns everyone on a team. 4) 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗖𝘂𝗿𝗶𝗼𝘀𝗶𝘁𝘆 ↳ They never stop asking "why is it built this way?" ↳ They question existing architectures and dig below the surface. 5) 𝗗𝗮𝗶𝗹𝘆 𝗗𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 ↳ Motivation disappears. Systems stay. ↳ They build habits that keep them shipping even on rough days. 6) 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 ↳ They break complex systems into understandable parts. ↳ They spot patterns others miss and make decisions with logic, not gut feel. 7) 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 ↳ Not everyone needs an MBA, but understanding the business impact of technical decisions is crucial. ↳ These engineers connect their work to actual valuable outcomes. 8) 𝗦𝗼𝗰𝗶𝗮𝗹 𝗜𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 ↳ Building trust across teams - dev, ops, security, product. ↳ They persuade without manipulation and get buy-in that moves projects forward. AI can generate code and automate deployments. But it can't replicate these human skills. 👀 If you're hiring engineers, look for these traits. 🧪 If you're building your career, start developing them now. They're what make you irreplaceable. Want more real talk about DevOps careers? Subscribe to our newsletter: https://bit.ly/4ndJVEy ♻️ Repost this to help engineers in your network thrive.

no cybersecurity experience? build it. trying to break in without job history? this is how you stand out: hands-on projects. certs help you learn. tryhackme + hackthebox help you practice. but if you’re not applying it— you’re missing the most important part. projects do what certs can’t: • show real proof of your skills • help you actually retain what you learn • teach you to break + fix things on your own and the best part? you don’t need fancy gear. build your experience: • for free—use virtualbox, vmware, etc. • or for cheap—grab old laptops or mini pcs need ideas? try these: • soc analyst – build a siem lab (splunk, elastic, wazuh). ingest logs. build dashboards. detect threats. • pentester – create a vuln lab. practice enum, privesc, and reporting. • security engineer – set up firewalls, segment networks, harden endpoints, write detection rules. • grc / auditor – explore nist or iso 27001. simulate a policy review or risk assessment. these projects = interview talking points, linkedin content, and portfolio gold. build your own experience. make “entry-level” irrelevant.

For those looking to start a career in data engineering or eyeing a career shift, here's a roadmap to essential areas of focus: 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 - 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Learn both full and incremental data extraction methods. - 𝗗𝗮𝘁𝗮 𝗟𝗼𝗮𝗱𝗶𝗻𝗴: - 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Master the techniques of insert-only, insert-update, and comprehensive insert-update-delete operations. - 𝗙𝗶𝗹𝗲𝘀: Understand how to replace files or append data within a folder. 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 - 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀: Acquire skills in manipulating CSV and Parquet file data with tools like Pandas and Polars. - 𝗦𝗤𝗟: Enhance your ability to transform data within PostgreSQL databases using SQL. This includes executing complex aggregations with window functions, breaking down transformation logic with Common Table Expressions (CTEs), and applying transformations in open-source databases such as PostgreSQL. 𝗗𝗮𝘁𝗮 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Develop the ability to create a Directed Acyclic Graph (DAG) using Python. - Gain expertise in generating logs for monitoring code execution and incorporate logging into databases like PostgreSQL. Learn to trigger alerts for failed runs. - Familiarize yourself with scheduling Python DAGs using cron expressions. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗞𝗻𝗼𝘄-𝗛𝗼𝘄 - Become proficient in using GIT for code versioning. - Learn to deploy an ETL pipeline (comprising extraction, loading, transformation, and orchestration) to cloud services like AWS. - Understand how to dockerize an application for streamlined deployment to cloud platforms such as AWS Elastic Container Service. 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗮𝗻𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Begin your learning journey here : https://lnkd.in/e5BxAwEu Mastering these foundational elements will equip you with the understanding and skills necessary to adapt to modern data engineering tools (aka the modern data stack) more effortlessly. Congratulations, you're now well-prepared to start interviewing for data engineer positions! While there are undoubtedly more advanced topics to explore such as data modeling , the courses and key areas highlighted above will give you a solid starting point for interviews.

Today we unveil our inaugural list of Skills on the Rise in Information Technology, a data-backed ranking of the 10 fastest-growing skills that IT workers should be investing in to get ahead in today’s world of work. Given the breadth of technical knowledge required for IT roles, the rankings highlight a number of specialized skills like AI Literacy (No. 1), Technical Documentation (No. 7) and LLM Application (No. 10). Confidential Information Management comes in at No. 2 — a nod to the critical role of data protection and privacy in today’s regulatory landscape. To compile the list, we looked at unique LinkedIn data to reveal the IT skills that professionals are increasingly adding and that companies are increasingly hiring for. Check out the full list of Skills on the Rise in the U.S. and our methodology here: https://lnkd.in/SkillsontheRise25US. Which skills stand out to you on the list? And what other IT skills do you see rising in demand right now? Tell us in the comments. #SkillsOnTheRise

Data Engineering isn't complicated if you learn the "why" before mastering the "how." Be it data cleaning, extraction, transformation, processing or indulging into building scalable architecture from chaos to clarity. Data Engineers emphasise on skills that cater to deliver quality before quantity. - Beginner Skills: a) Data Modeling: • Understand relational database concepts • Learn basic SQL querying • Practice creating Entity-Relationship Diagrams (ERDs) b) ETL Basics: • Grasp the concept of Extract, Transform, Load (ETL) • Use tools like Apache NiFi or Talend for simple data pipelines c) Data Storage: • Familiarize with different database types (SQL, NoSQL) • Learn basic data warehouse concepts d) Version Control: • Master Git for code management • Understand branching and merging strategies - Intermediate Skills: a) Big Data Processing: • Learn Apache Spark for distributed computing • Understand batch vs. stream processing b) Data Warehousing: • Implement star and snowflake schemas • Use tools like Amazon Redshift or Google BigQuery c) Data Integration: • Master Change Data Capture (CDC) techniques • Implement data quality checks and data cleansing d) Cloud Platforms: • Gain proficiency in AWS, GCP, or Azure data services • Implement cloud-native ETL solutions - Advanced Skills: a) Data Governance: • Implement data lineage tracking • Ensure compliance with regulations (GDPR, CCPA) b) Real-time Analytics: • Design streaming data architectures • Use technologies like Apache Kafka or Apache Flink c) Machine Learning Operations (MLOps): • Design data pipelines for ML model training and deployment • Implement feature stores for ML As a data engineer, I've found amazing projects to get hands-on exposure- 𝟭. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹 𝗮𝗻𝗱 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗘𝗧𝗟 𝗝𝗼𝗯 💻 https://lnkd.in/eq-e3_3J 𝟮. 𝗙𝗼𝗼𝘁𝗯𝗮𝗹𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 | 𝗔𝘇𝘂𝗿𝗲 𝗘𝗻𝗱-𝗧𝗼-𝗘𝗻𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 https://lnkd.in/eruSMc8f 𝟯. 𝗥𝗲𝗱𝗱𝗶𝘁 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - 𝗔𝗪𝗦 𝗘𝗻𝗱 𝘁𝗼 𝗘𝗻𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 💬 https://lnkd.in/ets_zWNV 𝟰. 𝗔𝗻 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝘄𝗶𝘁𝗵 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆, 𝗱𝗯𝘁 𝗦𝗼𝗱𝗮, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 📚 https://lnkd.in/e657xpWU 𝟱. 𝗘𝗧𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗶𝗻 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 📈 https://lnkd.in/eP8huQW3 𝟲. 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗧𝘄𝗶𝘁𝘁𝗲𝗿: 𝗞𝗮𝗳𝗸𝗮 𝗮𝗻𝗱 𝗦𝗽𝗮𝗿𝗸 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 🌐 https://lnkd.in/esVAaqtU 𝟳. 𝗘𝗧𝗟 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗼𝗻 𝗔𝗪𝗦 𝗖𝗹𝗼𝘂𝗱 ⚙ https://lnkd.in/ebgNtNRR 𝟴. 𝗔𝗺𝗮𝘇𝗼𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗨𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗮𝗻𝗱 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 ❄ https://lnkd.in/etXRckwW 𝟵. 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗥𝗲𝗮𝗹𝘁𝗶𝗺𝗲 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 - 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 📺 https://lnkd.in/ep4JWP-9 #data #engineering #bigdata #cloud

Here’s a small list of Kubernetes topics you will be learning vs what you will be performing in an actual job: Basics you will learn first by your-self / courses: 1. Kubernetes Fundamentals: - Understanding Kubernetes architecture - Pods, Nodes, and Clusters - Namespaces 2. Setup and Configuration: - Installing Minikube or Kubernetes on local machine - Understanding kubeadm, kops, and kubectl 3. Basic Objects and Concepts: - Deployments - Services - ReplicaSets - ConfigMaps and Secrets 4. Networking: - Cluster IP - NodePort - LoadBalancer - Ingress basics 5. Storage: - Persistent Volumes (PV) - Persistent Volume Claims (PVC) - Storage Classes 6. Basic Usage: - Creating and managing pods - Scaling applications - Rolling updates and rollbacks - Basic troubleshooting 7. Security: - Role-Based Access Control (RBAC) - Service Accounts 8. Monitoring and Logging: - Basics of monitoring with Prometheus - Logging with Elasticsearch, Fluentd, and Kibana (EFK stack) 9. Understanding YAML: - Writing basic YAML files for Kubernetes objects Usual production tasks: 1. Deployments: - Blue/Green deployments - Canary deployments - A/B testing 2. Networking: - Service Meshes (Istio, Linkerd) - Network Policies - Advanced Ingress configurations - CNI plugins (Calico, Flannel, Weave) 3. Storage: - StatefulSets - Dynamic provisioning - CSI (Container Storage Interface) 4. Security: - Pod Security Policies - Network Policies - Secrets management (Vault, Sealed Secrets) - Image security and scanning (Trivy, Clair) 5. Advanced Configuration: - Helm and Helm Charts - Kustomize - Operators and CRDs (Custom Resource Definitions) 6. Performance Tuning: - Resource limits and requests - Horizontal Pod Autoscaler (HPA) - Vertical Pod Autoscaler (VPA) - Cluster Autoscaler 7. Monitoring and Logging: - Advanced Prometheus configuration - Alerting with Alertmanager - Distributed tracing (Jaeger, OpenTelemetry) - Centralized logging 8. Cluster Management: - Multi-cluster management - Federation - Backup and restore strategies 9. CI/CD Pipelines: - Integrating CI/CD with Kubernetes (Jenkins X, Tekton) - GitOps (ArgoCD, Flux) 10. Disaster Recovery: - Backup and restore strategies - High availability and failover planning 11. Scaling and Capacity Planning: - Handling large-scale deployments - Capacity planning and resource optimization 12. Service Catalog and Broker: - Using the Kubernetes service catalog - Integrating external services 13. Compliance and Auditing: - Auditing with Kubernetes - Ensuring compliance with regulatory requirements 14. Troubleshooting: - Debugging complex issues - Analyzing logs and metrics - Using tools like k9s, kubectl-debug, and lens 15. Cost Management: - Cost optimization strategies - Using tools like Kubecost

The emergence of agentic AI doesn't eliminate the need for technical knowledge; it changes how that knowledge is applied. The skills that will become more valuable: • System architecture and design • Problem definition and evaluation • Data interpretation • Business impact assessment • AI prompt engineering and guidance The skills that will become less critical for most engineers: • Detailed syntax knowledge • Manual debugging of common issues • Writing boilerplate code • Configuring standard infrastructure Don't fight this evolution. Embrace it and position yourself at the higher-value layers. The best engineers will be those who can clearly define what they want built, not just those who can build it.