Data Engineering Career Transition

Build your future in Data Engineering.

A practical 90-day roadmap. Built around your working life.

Build the right skills, complete industry-standard projects and prepare for interviews while continuing your current job.

Register for Free Webinar

Free live session + 12-week roadmap for attendees

Bhabesh Lahkar
Bhabesh Lahkar Data Engineering Practitioner & Instructor
  • Build strong foundations
  • Create portfolio projects
  • Prepare for interviews
Portrait of Bhabesh Lahkar

Meet your instructor

Bhabesh Lahkar

Senior Data Engineer and Founder of DELaunchpad

20+ years in IT · 6+ years of international experience

I started my career in data in 2005, working with relational databases. Today, my work includes data platform architecture and building data pipelines with Azure, Databricks, and Snowflake. My international experience spans the Nordics, Singapore, London, and Germany.

Learn → Build → Practice → Prepare

At DELaunchpad, we help working professionals become job-ready for data engineering roles. They learn SQL and Python, build an end-to-end data pipeline, and practise explaining their design decisions in interviews. I share what works in production and where pipelines tend to fail.

Connect with me on LinkedIn
Get Started

Choose Your Next Move

Learn, practice and build your way into Data Engineering.

Learn

Build your Data Engineering foundation

Python • SQL • Linux • Git

Start Learning → Practice

Turn knowledge into practical skills

SQL • Data Modeling • Interview Practice

Practice Now → Build

Create projects that demonstrate your skills

ETL • Spark • Snowflake • Databricks • AI

Explore Projects →

How DELaunchpad helps you get started

Start with what you know and build toward a data engineering role at a pace that fits your workweek.

  • Unsure what to learn first?

    Follow a 12-week roadmap that starts with SQL and Python and shows you when to move on to pipelines and cloud tools.

  • Stuck watching tutorials?

    Build an end-to-end data pipeline so you have a portfolio project you can demonstrate and explain.

  • Unsure how to prepare for interviews?

    Practise interview questions and learn to explain your project decisions, from data modelling to pipeline design.

About the Platform

Why DELaunchpad?

Data Engineering is one of the most in-demand and high-paying careers in tech — but most learners struggle with confusion and lack of direction. DELaunchpad cuts through the noise and provides clarity through a focused, structured path — with step-by-step guidance and support at every stage — so you can build confidently and get to your dream career faster.

Structured Roadmaps

Follow step-by-step learning paths crafted by experienced data engineers, so you always know what to learn next.

Curated Resources

Every book, course, and article on this platform has been vetted. No filler, no fluff — only the best content.

Career Guidance

Get actionable advice on building your portfolio, acing interviews, and landing your first Data Engineering role.

Community-Driven

Join a global community of learners and practitioners who are navigating the same journey as you.

Learning Path

Your Data Engineering Journey

A phased learning path designed to take you from complete beginner to job-ready data engineer.

PPerceiveDiscover the role, expectations & mindset
AAcquireBuild strong fundamentals & skills
TTrainHands-on projects & real-world scenarios
HHiredCrack interviews & land the job
1

Foundations

Master the building blocks: Python programming, SQL databases, data structures, and command-line basics.

  • Python
  • SQL
  • Linux CLI
  • Git & GitHub
2

Data Pipelines & ETL

Build your first data pipelines, learn ETL/ELT patterns, and understand data warehousing concepts.

  • Apache Airflow
  • dbt
  • ETL/ELT
  • Data Warehousing
3

Big Data & Streaming

Process massive datasets with Spark and handle real-time streaming workloads with Kafka.

  • Apache Spark
  • Apache Kafka
  • Flink
  • Delta Lake
4

Cloud & Infrastructure

Deploy production-grade pipelines using cloud platforms, container orchestration, and IaC tools.

  • AWS / GCP / Azure
  • Docker & Kubernetes
  • Terraform
  • CI/CD
5

Portfolio & Job Search

Build end-to-end portfolio projects, craft your resume, and prepare for technical interviews.

  • Capstone Projects
  • Resume Building
  • System Design
  • Interview Prep
Learning Resources

Learning Resources & Recommended Books

Explore recommended third-party tutorials, documentation, free courses and books. Some books may require purchase.

Coming Soon

SQL for Data Engineers

Master SQL through real-world data engineering problems, hands-on practice, and interview-focused challenges.

8–10 hours Beginner → Intermediate Enrollment opening soon
Course preview →
Course

Data Engineering Zoomcamp

Free 9-week cohort course covering the entire DE stack, from ingestion to orchestration.

9 weeks Free
View free course →
Book

Fundamentals of Data Engineering

The definitive guide to the data engineering lifecycle by Joe Reis & Matt Housley.

~400 pages Bestseller
View book →
Book

Python Data Science Handbook

A practical guide to using Python for data analysis and visualization.

Free Online Essential
Learn more →
Book

Automate the Boring Stuff with Python

Learn Python by automating everyday tasks.

Free Online Beginner
Learn more →
Interactive Tutorial

SQLBolt

Interactive SQL lessons for beginners.

Self-paced Beginner
Learn more →
SQL Tutorial

Mode SQL Tutorial

Hands-on SQL tutorial with real-world examples.

Self-paced Intermediate
Learn more →
Documentation

Apache Spark Documentation

Official guide to learning Spark for big data processing.

Reference Advanced
Learn more →
Tutorials

Snowflake Quickstarts

Step-by-step tutorials for Snowflake data platform.

Self-paced Beginner
Learn more →
Training

Databricks Learning

Free training resources for Databricks and Spark.

Self-paced Free
Learn more →
Learn more →
Project

End-to-End Batch Pipeline

Build a real-world pipeline: ingest public data, transform with dbt, visualize in Metabase.

2–3 hours Beginner
Explore architecture →
Coming Soon

Build Your First AI SQL Assistant with Streamlit

Build a practical AI-powered SQL assistant using Streamlit, Python, an LLM and a database. Turn natural-language questions into SQL and interactive data insights.

6–8 hours Beginner → Intermediate Enrollment opening soon
Course preview →
Blog

DELaunchpad Community Updates

Join the DELaunchpad community for learning resources and future academy updates.

Community updates Free
Join community →
Project guide coming soon
Project Architecture Preview

Featured Project Blueprint

Explore the architecture of a batch data pipeline. The full hands-on guide is coming soon.

01 Architecture Preview

End-to-End Data Engineering Pipeline

Build a complete batch data pipeline from raw data to analytics-ready data.

Python • Azure • ADLS • Databricks • Snowflake

Outcome: Build and explain an end-to-end Data Engineering architecture.

Explore Architecture → 02 Coming Soon

Build an AI SQL Assistant

Turn natural-language questions into SQL and database insights using AI.

Python • OpenAI • SQL • Streamlit

Outcome: Build a portfolio project combining Data Engineering and AI.

Course Preview →
Architecture preview Level: Beginner to Intermediate Hands-on guide coming soon

What This Blueprint Covers

Understand the components of a real-world batch pipeline before building the complete project.

  • Ingest data from APIs or public datasets
  • Transform data using SQL and dbt
  • Store data in a cloud data warehouse
  • Build dashboards for insights

Pipeline Architecture

Follow the data from source to dashboard in this architecture preview.

  1. Ingestion Layer

    Extract data using Python scripts or APIs

  2. Storage Layer

    Load raw data into a data warehouse (BigQuery, Snowflake, or Postgres)

  3. Transformation Layer

    Clean and model data using dbt. Apply tests and modular design.

  4. Serving Layer

    Build dashboards using Metabase

Tools & Technologies

  • Python
  • SQL
  • dbt
  • Cloud Data Warehouse (BigQuery / Snowflake)
  • Metabase

What You'll Learn

  • End-to-end data pipeline design
  • ETL/ELT concepts
  • Writing production-level SQL
  • Data modeling best practices
  • Building portfolio-ready projects

Planned Project Deliverables

  • Complete pipeline project
  • GitHub repository with clean code
  • Dashboard with insights
  • Architecture diagram

Why This Matters

Batch pipelines are widely used in industry to process and analyze data. This architecture preview introduces the components used in practical data engineering projects.

Knowledge Hub

Data Engineering Knowledge Hub

These technologies form the backbone of modern data engineering. Master them and you'll be equipped for almost any DE role.

Foundations

Python

The lingua franca of data engineering. Used for scripting, pipeline development, data transformations, and automation.

  • ETL scripting
  • Data wrangling with Pandas
  • API integrations
Foundations

SQL

The universal language for querying, transforming, and managing data in relational and analytical databases.

  • Data querying & analysis
  • dbt transformations
  • Window functions & CTEs
Modern Data Stack

Apache Spark

Distributed processing engine for large-scale batch and streaming data workloads at petabyte scale.

  • Big data processing
  • Machine learning pipelines
  • Spark Structured Streaming
Data Engineering

Apache Airflow

The de facto standard for orchestrating complex data pipelines using directed acyclic graphs (DAGs).

  • Pipeline orchestration
  • Scheduling & monitoring
  • DAG-based workflows
Modern Data Stack

Apache Kafka

High-throughput distributed event streaming platform for real-time data pipelines and messaging.

  • Event streaming
  • Real-time ingestion
  • Change data capture
Cloud

Cloud Platforms

AWS, GCP, and Azure provide the managed services, storage, and compute that power modern data stacks.

  • S3 / GCS / ADLS storage
  • BigQuery / Redshift / Synapse
  • Serverless functions

Curious how AI fits into Data Engineering? See how AI is becoming part of the stack →

What's Next

Data Engineering is Evolving.
AI is becoming part of the stack.

Emerging skills every Data Engineer will benefit from understanding.

AI for Data Engineers

  • LLMs
  • Prompt Engineering
  • AI APIs
  • RAG

AI-Powered Data Tools

  • SQL Assistants
  • Data Assistants
  • AI Career Tools

Snowflake AI

  • Cortex
  • Cortex Analyst
  • Cortex Search
  • Cortex Agents
Career Transition

Your Roadmap to a DE Role

Whether you're coming from software engineering, analytics, science, or a completely unrelated field — here's how to make the move.

Assess Your Starting Point

Identify what you already know (SQL? Python? Cloud?) and where the gaps are. This saves months of wasted effort.

Follow the Structured Roadmap

Use our phased curriculum to learn systematically. Aim to spend 2–3 hours per day if transitioning full-time.

Build a Public Portfolio

Publish 2–3 end-to-end projects on GitHub. Real projects beat certificates every time in DE interviews.

Network & Apply Strategically

Connect with data engineers on LinkedIn, contribute to open-source, and apply to roles that match your stack.

Common Backgrounds

  • 💻 Software Engineers
  • 📊 Data Analysts
  • 🔬 Data Scientists
  • 🎓 Computer Science Graduates
  • 🔬 Science / Engineering Grads

Know Where You Stand.

Understand your strengths, identify your gaps, and know what to work on next.

Example Career Readiness Snapshot
  • SQLStrong
  • PythonDeveloping
  • Data ModelingDeveloping
  • SparkNext Focus
  • CloudNext Focus
  • ProjectsDeveloping
  • Interview ReadinessNext Focus
Community & Newsletter

Join the DELaunchpad Data Engineering Community

Get weekly curated resources, job opportunities, and community updates delivered straight to your inbox. No spam, unsubscribe anytime.

  • ✅ Weekly curated DE articles & tools
  • ✅ Exclusive project ideas & datasets
  • ✅ Connect with fellow Data Engineering learners
  • ✅ Data Engineering career guidance & interview preparation tips