🚀 Start Learning - Data Engineering Roadmap

Begin your comprehensive data engineering learning journey by choosing your perfect specialization path.

Usage

/start-learning

What This Command Does

This interactive command will:

1. Assess Your Background - Understand your current experience level
2. Identify Your Interests - Discover which data role excites you
3. Evaluate Time Commitment - Plan realistic learning timelines
4. Create Your Roadmap - Generate personalized 12-40 week learning path
5. Set Initial Goals - Define first month objectives
6. Recommend Resources - Suggest curated learning materials

🎯 Available Data-Focused Roles

1. Data Engineer ⭐ PRIMARY ROLE

Best For: Those who love building systems at scale

• Master data pipelines, ETL systems, databases, big data
• 40-week learning path (12-18 months for complete beginners)
• Technologies: Python, SQL, Spark, Airflow, Kafka, Cloud Platforms
• Salary: $80K-$300K+
• Career Impact: Build the infrastructure that powers analytics & ML

2. Backend Engineer

Best For: Those building data ingestion APIs and services

• Server-side systems, APIs, databases, microservices
• 36-week learning path (9-12 months for complete beginners)
• Technologies: Python/Node.js, PostgreSQL, Redis, Docker, Kubernetes
• Salary: $70K-$250K+
• Career Impact: Create APIs that feed data platforms

3. Data Scientist

Best For: Math enthusiasts who love statistics and ML

• Machine learning, statistics, data analysis, predictions
• 40-week learning path (12-18 months for complete beginners)
• Technologies: Python, Pandas, Scikit-learn, TensorFlow, SQL
• Salary: $80K-$300K+
• Career Impact: Turn data into dollars through insights & predictions

4. ML & AI Engineer

Best For: Those passionate about cutting-edge AI

• Deep learning, LLMs, generative AI, agents
• 40-week learning path (14-20 months for complete beginners)
• Technologies: PyTorch, Transformers, LangChain, LLMs
• Salary: $100K-$400K+
• Career Impact: Build intelligent systems that solve hard problems

5. Cloud Data Engineer

Best For: Those who love designing large-scale architectures

• Cloud platforms, data warehouses, cost optimization
• 36-week learning path (10-14 months for complete beginners)
• Technologies: AWS/GCP/Azure, Snowflake, BigQuery, Terraform
• Salary: $85K-$300K+
• Career Impact: Design cloud-native data solutions

6. DevOps Engineer

Best For: Those who love automation and reliability

• Infrastructure automation, CI/CD, Kubernetes, monitoring
• 40-week learning path (10-15 months for complete beginners)
• Technologies: Linux, Docker, Kubernetes, Terraform, CI/CD
• Salary: $75K-$280K+
• Career Impact: Keep data systems running reliably 24/7

🎯 How to Use This Command

Step 1: Assess Your Current Level

Answer these questions:

• Beginner: No programming experience
• Intermediate: Some programming (1-2 years)
• Advanced: Strong programming + CS background (3+ years)
• Transition: From another field into data

Step 2: Choose Your Primary Interest

• I want to build systems → Data Engineer or DevOps Engineer
• I want to extract insights → Data Scientist
• I want cutting-edge AI → ML & AI Engineer
• I want to design architectures → Cloud Data Engineer
• I want to build APIs → Backend Engineer

Step 3: Assess Time Commitment

• Part-time: 10-15 hours/week → 14-24 months to junior level
• Moderate: 20-30 hours/week → 9-15 months to junior level
• Full-time: 40+ hours/week → 6-12 months to junior level

Step 4: Get Your Personalized Roadmap

Based on your answers, you'll receive:

• ✅ Recommended role with detailed overview
• ✅ 4-8 week learning path (structured by phases)
• ✅ Must-learn technologies (prioritized list)
• ✅ Project ideas for your level
• ✅ Estimated salary progression
• ✅ Interview preparation timeline
• ✅ Next commands to use

📊 Role Comparison Matrix

Aspect	Data Engineer	Backend Engineer	Data Scientist	ML Engineer	Cloud Engineer	DevOps Engineer
Learning Curve	🟡 Moderate	🟡 Moderate	🔴 High (Math)	🔴 Very High	🟡 Moderate	🟡 Moderate
Salary Potential	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Job Market	⭐⭐⭐⭐⭐ Hot	⭐⭐⭐⭐ Very Hot	⭐⭐⭐⭐⭐ Hot	⭐⭐⭐⭐⭐ Hottest	⭐⭐⭐⭐⭐ Hot	⭐⭐⭐⭐⭐ Hot
Remote Friendly	✅ Very	✅ Very	✅ Very	✅ Very	✅ Very	✅ Very
Work-Life Balance	🟢 Good	🟢 Good	🟡 Medium	🟡 Medium	🟢 Good	🟢 Good

💡 Quick Decision Guide

Choose Data Engineer if you:

• ✅ Love solving scalability problems
• ✅ Want to understand data flows end-to-end
• ✅ Enjoy building production systems
• ✅ Like working with modern tools (Spark, Kafka, Airflow)
• ✅ Want consistent $250K+ salaries

Choose Backend Engineer if you:

• ✅ Love building API services
• ✅ Enjoy working with databases
• ✅ Want to build features that users interact with
• ✅ Like microservices and distributed systems

Choose Data Scientist if you:

• ✅ Love mathematics and statistics
• ✅ Enjoy hypothesis testing and experimentation
• ✅ Want to answer business questions with data
• ✅ Like Python and statistical modeling

Choose ML & AI Engineer if you:

• ✅ Are passionate about AI and LLMs
• ✅ Enjoy cutting-edge research
• ✅ Want to build intelligent systems
• ✅ Love PyTorch, transformers, and deep learning

Choose Cloud Engineer if you:

• ✅ Love designing large-scale systems
• ✅ Enjoy cost optimization and architecture
• ✅ Want to master cloud platforms
• ✅ Like automation and infrastructure

Choose DevOps Engineer if you:

• ✅ Love automation and reliability
• ✅ Enjoy infrastructure and systems
• ✅ Want to enable teams to deploy safely
• ✅ Like monitoring and incident response

🚀 Example Interaction

User: /start-learning

Claude: Welcome to your data engineering learning journey! Let me help you find your perfect path.

I'll ask you a few quick questions:

1. What's your current programming experience?
   A) None/beginner
   B) 1-2 years
   C) 3+ years strong
   D) Transitioning from another field

2. What excites you most?
   A) Building scalable systems
   B) Extracting insights from data
   C) Cutting-edge AI/LLMs
   D) Designing cloud architectures
   E) Infrastructure & automation
   F) Building APIs

3. How much time can you dedicate per week?
   A) 10-15 hours
   B) 20-30 hours
   C) 40+ hours (full-time)

Please answer with A, B, C (and D if applicable) for each question!

📋 What Happens Next

After responding to the interactive assessment, you'll get:

Your Personalized Learning Plan

• Recommended role (with why it's perfect for you)
• Your realistic timeline to junior-level competency
• Weekly study plan for month 1
• Technologies to focus on (prioritized)

Your First Month Milestones

• Week 1-2: Foundation concepts
• Week 3-4: Your first project
• Resources for each week
• Success metrics

Quick Links to Other Commands

• /roadmap-status - Track your long-term progress
• /skill-deep-dive [skill] - Master a specific technology
• /project-ideas - Build projects for your level
• /choose-role - Change your focus later
• /assessment - Evaluate your knowledge gaps

❓ FAQ

Q: Can I switch roles later? A: Yes! Run /choose-role anytime to change your path.

Q: What if I don't fit into one role? A: Many people combine roles! Data Engineers + ML Knowledge = highly valued.

Q: How long until I can get a job? A: Most people reach junior level in 12-18 months with consistent 20+ hours/week.

Q: Do I need a degree? A: No. Portfolio projects and practical skills matter more than degrees.

Q: What if I want to learn multiple specializations? A: Great! Start with one, then use /skill-deep-dive to expand.

🎓 Next Steps

1. Run this command with your answers
2. Get your personalized roadmap
3. Review first month plan
4. Use /skill-deep-dive for detailed topics
5. Build projects with /project-ideas
6. Track progress with /roadmap-status

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Ready to begin your journey? Answer the questions above, and let's create your perfect learning path!

Need something specific? Try:

• /choose-role - Pick a specific role
• /skill-deep-dive [topic] - Deep dive into one skill
• /project-ideas - Get hands-on projects
• /interview-prep - Prepare for jobs