Terraform skills boost salary up to $200K

🚀 𝗧𝗵𝗲 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿’𝘀 𝗧𝗼𝗼𝗹𝗸𝗶𝘁 — 𝟭𝟬 𝗧𝗼𝗼𝗹𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻’𝘁 𝗜𝗴𝗻𝗼𝗿𝗲 If you’re a backend developer — your tools define your impact. It’s not just about writing code anymore — it’s about how you design, scale, and automate what runs behind it. That’s why I put together this visual — 💡 10 tools every backend engineer ends up relying on at some point in their journey. ⚙️ 𝘾𝙤𝙧𝙚 𝙎𝙩𝙖𝙘𝙠 𝙏𝙝𝙖𝙩 𝙋𝙤𝙬𝙚𝙧𝙨 𝙈𝙤𝙙𝙚𝙧𝙣 𝘽𝙖𝙘𝙠𝙚𝙣𝙙𝙨: 🐳 Docker → Containerization for consistent environments ☸️ Kubernetes → Orchestration & scaling 🐘 PostgreSQL → Reliable relational data handling ⚡ Redis → In-memory caching & queues 🧩 Kafka → Event streaming for async communication 📊 Prometheus + Grafana → Monitoring & observability 🌐 Nginx → Load balancing & API gateway 🏗️ Terraform → Infrastructure as Code 🧪 Jest / Mocha / Supertest → Testing automation ☁️ Cloud (AWS / GCP / Azure) → Global scalability & resilience 💡 𝘛𝘰𝘰𝘭𝘴 𝘥𝘰𝘯’𝘵 𝘭𝘪𝘮𝘪𝘵 𝘺𝘰𝘶 — 𝘵𝘩𝘦𝘺 𝘢𝘮𝘱𝘭𝘪𝘧𝘺 𝘺𝘰𝘶𝘳 𝘪𝘥𝘦𝘢𝘴. ⚙️ 𝘊𝘰𝘯𝘯𝘦𝘤𝘵 𝘵𝘩𝘦𝘴𝘦 𝘵𝘰𝘰𝘭𝘴 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦, 𝘰𝘣𝘴𝘦𝘳𝘷𝘢𝘣𝘭𝘦, 𝘢𝘯𝘥 𝘳𝘦𝘭𝘪𝘢𝘣𝘭𝘦 𝘴𝘺𝘴𝘵𝘦𝘮𝘴. These are some of the most widely used tools across production systems — but every team and project has its own stack. You might use different tools — and that’s totally valid. What really matters is knowing how to pick the right tool for the right problem. 📎 Save this post to revisit when you’re preparing for interviews, building your next side project, or scaling something big at work. #Backend #DevOps #Cloud #SystemDesign #Engineering #SoftwareDevelopment #Docker #Kubernetes #PostgreSQL #Redis #Kafka #Infrastructure #Observability #Terraform #Architecture #Programming #DecodeWithSaurav

Senior DevOps Engineer interview questions!! AWS 1. What are all the services AWS provides to achieve security? 2. How would you deploy a python code base with heavy libraries into AWS Lambda? Understand AWS Lambda limitations!! 3.How do you recover lost state? S3, versioning 4.How do you share information between two pipelines? 5. How do you control deployments? 6. Python code for certificate rotation? 7. Python code for testing an API throughput? 8. What are the pillars of AWS Well Architected framework? 9. How would you achieve Cost Optimisation in AWS? 10. How would you manage more than one AWS accounts? #SeniorDevOpsEngineer #SRE #SiteReliabilityEngineer #DevOpsInterview #InterviewQuestions

🚀 The Power of Python + AWS: Building Scalable Cloud Solutions 🚀 As a software engineer, I'm constantly amazed by how Python and AWS complement each other perfectly for building robust, scalable applications. 💡 Here's why this combo is a game-changer: 🐍 Python's Versatility: • Simple, readable syntax that accelerates development • Rich ecosystem with libraries like Boto3 for AWS integration • Perfect for automation, data processing, and ML workloads ☁️ AWS's Cloud Power: • Lambda for serverless Python functions • EC2 for scalable compute resources • S3 for limitless storage • RDS for managed databases • SageMaker for ML model deployment 🔥 Real-World Impact: Whether you're automating infrastructure with Python scripts, building serverless APIs, or deploying ML models - this stack delivers! 💼 As someone who works with these technologies daily, I can say: The learning curve is worth it. The possibilities are endless. What's your favorite Python + AWS use case? Drop your thoughts below! 👇 #Python #AWS #CloudComputing #DevOps #ServerlessArchitecture #MachineLearning #TechInnovation #SoftwareEngineering

I just completed a Python automation project that solves a common challenge in shared AWS environments—inconsistent EC2 instance naming. Imagine this problem situation: Multiple departments are provisioning EC2 instances without standardized naming conventions, making it difficult to track ownership, manage costs, and maintain proper resource governance. To solve that problem I built a Python script that automatically generates unique, standardized EC2 instance names by: 1. Validating department names against an approved list (Marketing, Accounting, FinOps). 2. Handling case-insensitive input for better user experience. 3. Generating random 4-character alphanumeric suffixes to ensure uniqueness . 4. Outputing as many instance names as the on user needs, allowing for quick provisioning of multiple instances at once. Some of my key technical learnings were around loops: while loops, for loops, and iteration logic. I also learned a lot about string manipulation and input validation. Finally I learned how to randomize effectively in Python. This project reinforced an important lesson: automation doesn't need to be complex to deliver real business value. Sometimes a well-designed script can solve organizational problems more effectively than elaborate systems. The experience strengthened my foundation in Python fundamentals and gave me building blocks for more advanced cloud automation and DevOps workflows. Excited to continue building on these skills as I work toward more complex AWS automation projects! Check out the embedded link in my article if you want to see my code on GitHub. Shoutout to my program:Level Up In Tech Broadus Palmer Janita Green Williamson Joe Stephens Tait Hoglund Brett D. Troy Ingram #Python #AWS #CloudComputing #DevOps #Automation #EC2 #CloudEngineering #TechProjects #Loops #Iterations

When I first heard about Docker, I’ll be honest — it sounded intimidating. Containers? Images? Volumes? I thought it was something only DevOps engineers used. 😅 But once I tried it, everything changed. As a backend developer (1.5 years experience) working with Node.js + TypeScript + PostgreSQL + MongoDB, I realized one major pain point: > “It works on my machine” 😭 Then Docker entered the chat. 💥 I started small — Created a Dockerfile for my Node.js app Added PostgreSQL and Redis containers Used Docker Compose to run everything together Suddenly, my local setup = production setup. No more environment mismatches. No more dependency chaos. 💡 Why Docker matters: ✅ Reproducible environments ✅ Easy deployment on AWS ECS/EKS ✅ Isolated services that scale better Now, every new project I start begins with a Dockerfile. And honestly, I wish I’d learned it earlier. If you’re a backend dev and still hesitant to try Docker — take the leap. It’ll change how you think about backend environments forever. #BackendDeveloper #NodeJS #TypeScript #Docker #DevOps #Microservices #PostgreSQL #MongoDB #AWS #SoftwareEngineer #OpenToWork #BackendLife

🌥️ 𝐇𝐨𝐰 𝐭𝐨 𝐒𝐭𝐚𝐫𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 #𝐀𝐖𝐒 𝐚𝐬 𝐚 #𝐉𝐚𝐯𝐚 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 🚀 If you're a Java developer, learning AWS can take you from writing code → to deploying scalable, production-grade systems. Here’s a clear and practical roadmap to get started 👇 ✅ 1. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝑪𝒐𝒓𝒆 𝑪𝒍𝒐𝒖𝒅 𝑩𝒖𝒊𝒍𝒅𝒊𝒏𝒈 𝑩𝒍𝒐𝒄𝒌𝒔 𝑳𝒆𝒂𝒓𝒏 𝒕𝒉𝒆 𝒇𝒖𝒏𝒅𝒂𝒎𝒆𝒏𝒕𝒂𝒍𝒔: 𝑪𝒐𝒎𝒑𝒖𝒕𝒆: EC2, Lambda 𝑺𝒕𝒐𝒓𝒂𝒈𝒆: S3 𝑫𝒂𝒕𝒂𝒃𝒂𝒔𝒆𝒔: RDS, DynamoDB 𝑵𝒆𝒕𝒘𝒐𝒓𝒌𝒊𝒏𝒈: VPC, Security Groups This builds the foundation for everything you’ll deploy. ✅ 2. 𝑮𝒐 𝑯𝒂𝒏𝒅𝒔-𝑶𝒏 𝑸𝒖𝒊𝒄𝒌𝒍𝒚 Create a Free Tier AWS account Deploy a simple #SpringBoot app Connect it to RDS (#MySQL/#PostgreSQL) Store files in S3 Don’t just watch tutorials — deploy something real. ✅ 3. 𝑾𝒐𝒓𝒌 𝒘𝒊𝒕𝒉 𝑨𝑾𝑺 𝑺𝑫𝑲 𝒇𝒐𝒓 𝑱𝒂𝒗𝒂 Use AWS services directly from your code: Upload/download files to S3 Send emails using SES Trigger processes with SQS / SNS This connects your Java backend to the cloud. ✅ 4. 𝑳𝒆𝒂𝒓𝒏 𝑫𝒆𝒑𝒍𝒐𝒚𝒎𝒆𝒏𝒕 𝑶𝒑𝒕𝒊𝒐𝒏𝒔 Beginner → Advanced sequence: Elastic Beanstalk (Easy App Hosting) ECS + Fargate (Container-Based Deployments) EKS (Kubernetes) if you already use K8s ✅ 5. 𝑩𝒐𝒐𝒔𝒕 𝑺𝒌𝒊𝒍𝒍𝒔 𝒘𝒊𝒕𝒉 𝑪𝑰/𝑪𝑫 Learn how to automate deployments: #GitHub Actions #AWS CodePipeline #Jenkins This takes you into the #DevOps lane 💡 ✅ 6. 𝑨𝒊𝒎 𝒇𝒐𝒓 𝑪𝒆𝒓𝒕𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏 (𝑶𝒑𝒕𝒊𝒐𝒏𝒂𝒍 𝒃𝒖𝒕 𝑷𝒐𝒘𝒆𝒓𝒇𝒖𝒍) 🎯 #AWS Developer Associate Great for credibility + job interviews. ✅ 7. 𝑼𝒔𝒆 #𝑨𝑰 𝑻𝒐𝒐𝒍𝒔 𝒕𝒐 𝑳𝒆𝒂𝒓𝒏 𝑭𝒂𝒔𝒕𝒆𝒓 #ChatGPT for debugging & architecture guidance #GitHub Copilot for code generation #Amazon Q Developer for AWS-specific support 🎯 𝑭𝒊𝒏𝒂𝒍 𝑻𝒉𝒐𝒖𝒈𝒉𝒕 𝒀𝒐𝒖𝒓 𝑱𝒂𝒗𝒂 𝒆𝒙𝒑𝒆𝒓𝒊𝒆𝒏𝒄𝒆 𝒊𝒔 𝒂𝒍𝒓𝒆𝒂𝒅𝒚 50% 𝒐𝒇 𝒕𝒉𝒆 𝒋𝒐𝒖𝒓𝒏𝒆𝒚. 𝑨𝑾𝑺 𝒉𝒆𝒍𝒑𝒔 𝒚𝒐𝒖 𝒅𝒆𝒑𝒍𝒐𝒚, 𝒔𝒄𝒂𝒍𝒆, 𝒔𝒆𝒄𝒖𝒓𝒆, 𝒂𝒏𝒅 𝒂𝒖𝒕𝒐𝒎𝒂𝒕𝒆 𝒓𝒆𝒂𝒍-𝒘𝒐𝒓𝒍𝒅 𝒂𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏𝒔. 𝑻𝒉𝒊𝒔 𝒊𝒔 𝒘𝒉𝒆𝒓𝒆 𝒚𝒐𝒖𝒓 𝒃𝒂𝒄𝒌𝒆𝒏𝒅 𝒔𝒌𝒊𝒍𝒍𝒔 𝒃𝒆𝒄𝒐𝒎𝒆 𝒆𝒏𝒕𝒆𝒓𝒑𝒓𝒊𝒔𝒆-𝒓𝒆𝒂𝒅𝒚. 🚀 #AWS #Java #SpringBoot #CloudComputing #Microservices #DevOps #BackendDeveloper #SoftwareEngineering #CloudNative #CareerGrowth #LearningEveryday #TechCommunity #java #JavaDeveloper #Hiring #OpenToWork

Project: Terraform-based AWS Lambda + EventBridge + SNS Daily Reminder I recently built and deployed a fully automated daily reminder system on AWS using Terraform, Python, and EventBridge. The setup provisions all resources — including Lambda functions, IAM roles, SNS topics, and EventBridge schedules — entirely as code. Every day at 2:30 PM Toronto time, EventBridge triggers the Lambda function, which publishes notifications through Amazon SNS (SMS). Key highlights: • Infrastructure as Code with Terraform • Event-driven automation using CloudWatch/EventBridge • Secure IAM policies for least-privilege execution • Serverless Python function deployment • End-to-end automated SMS notifications 🔗 GitHub Repository: https://lnkd.in/eFyzfEvT This project showcases how easily daily operational workflows can be automated with serverless + IaC, reducing manual overhead while keeping infrastructure reproducible and scalable. #Terraform #AWS #DevOps #Lambda #EventBridge #SNS #CloudAutomation #InfrastructureAsCode #Serverless #Python

𝗛𝗼𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱 If you’re starting your journey into backend engineering, here’s a simple roadmap to focus on what really matters — no noise, just essentials. 🧩 1️⃣ Fundamentals Understand how backend connects to frontend — Client-Server Model, DNS, APIs, and how data moves across systems. 💻 2️⃣ Learn a Core Language Pick one and go deep — Java, Python, Go, Node.js, or Rust. It’s not about learning all, it’s about mastering one. 🗄️ 3️⃣ Databases Get comfortable with SQL and NoSQL, caching with Redis, and query optimization. ⚙️ 4️⃣ APIs & Communication Learn REST, GraphQL, and gRPC — these are how microservices talk. ☁️ 5️⃣ DevOps & Cloud Get hands-on with Docker, Kubernetes, and deployment on AWS, Azure, or GCP. Automation is your secret weapon. 🧠 Pro Tip: Don’t just read tutorials — build small projects, deploy them, and monitor how they behave. Because theory builds knowledge, but deployment builds experience. 💡 #BackendDevelopment #JavaDeveloper #SpringBoot #Microservices #CloudComputing #Docker #Kubernetes #DevOps #APIDesign #GraphQL #gRPC #Java17 #FullStackDeveloper #PlatformEngineering #AWS #Azure #GCP #DatabaseEngineering #PostgreSQL #Redis #SoftwareArchitecture #Serverless #CloudNative #Java25 #Performance #TechHiring #EngineeringLeadership #SystemDesign #ContractJobs #C2C #H1B #W2

How to Learn Backend Development — Backend development isn’t just about writing APIs — it’s about designing systems that scale, communicate, and never fail under pressure. Here’s how I guide new developers entering backend engineering 👇 1️⃣ Start with Fundamentals Understand how the web works — client-server model, DNS, HTTP, and networking basics. 2️⃣ Master Core Languages Pick one — Java, Python, Go, or Rust — and learn how to structure clean, testable backend code. 3️⃣ Get Comfortable with Databases Understand SQL, NoSQL, and NewSQL. Learn indexing, caching, and query optimization. 4️⃣ Learn API Design Design REST and GraphQL APIs. Later, explore gRPC and SOAP for inter-service communication. 5️⃣ DevOps Integration Get hands-on with Docker, Kubernetes, CI/CD pipelines, and IaC (Terraform, Ansible) to deploy and monitor services. 6️⃣ Cloud & Scalability Experiment on AWS, Azure, or GCP. Learn to manage load balancing, caching, and fault tolerance. Remember — a great backend engineer doesn’t just code; they engineer reliability, performance, and resilience. #BackendDevelopment #Java #SpringBoot #Microservices #APIs #DevOps #AWS #Docker #Kubernetes #SoftwareEngineering #FullStackDeveloper #CloudComputing