Summary of "How I would learn Data Engineering in 2025 (if I could start over) – Built by a Data Engineer"

Summary of “How I would learn Data Engineering in 2025 (if I could start over) – Built by a Data Engineer”

Main Ideas and Concepts

This video is a comprehensive guide by Barra, a seasoned data engineer with over a decade of experience, on how to start and progress in data engineering in 2025. Barra breaks down the journey into three clear phases, focusing on essential skills, job acquisition, and professional growth.

Detailed Roadmap and Methodology

Introduction: What is a Data Engineer?

Data engineers build and maintain the “engine room” of data-driven companies.
They move, transform, and store massive amounts of data behind the scenes.
The role is distinct from building dashboards or front-end applications.
Key question for aspirants: Are you excited by moving and transforming data and building data ecosystems?

Phase 1: Core Skills (Technical Foundations)

Focus on mastering only what matters most for data engineers, distilled into 7 key skills:

SQL (Structured Query Language)
- Non-negotiable foundational skill.
- Used to query, clean, and transform data.
- Recommended to learn SQL Server (friendly, similar to cloud platforms like Azure Synapse).
- Must-learn SQL concepts: querying, joins, window functions, Common Table Expressions (CTEs).
- Barra offers a free 30-hour SQL course on his channel.
Python
- The dominant programming language for data engineering.
- Used for connecting to data sources, transforming data, automating tasks, and building workflows.
- Recommended IDE: Visual Studio Code (preferred for serious projects) or Jupyter Notebook.
- Core Python skills: variables, data types, data structures (lists, tuples, dictionaries, sets), functions, file handling (CSV, JSON, XML, Parquet, Delta).
- Barra is developing a free Python course on his channel.
Apache Spark and PySpark
- Essential for processing massive data sets.
- Learn Spark architecture, core objects (RDDs, DataFrames), and PySpark operations including SQL-style queries.
- Recommended learning environment: Databricks Community Edition (free, cloud-ready).
- Mastering PySpark prepares you for big data projects.
Git (Version Control)
- Critical for collaboration and code management in teams.
- Learn repository creation, commits, branching, merging, pull/push/clone operations, and conflict resolution.
- Understanding Git improves teamwork and project workflow.
Data Pipelines and Orchestration
- Central responsibility of a data engineer: building systems that move and transform data reliably.
- Learn ETL (Extract, Transform, Load) and ELT concepts.
- Tools: Apache Airflow (free/open source), Databricks Workflows (premium), Azure Data Factory (premium).
- Skills include error handling, logging, scheduling, and automation of pipelines.
Modern Data Platforms
- Focus on one major cloud data platform: Databricks, Snowflake, or Google BigQuery.
- Learn basics of the platform: creating notebooks, clusters, SQL usage, workflows, and catalog management.
- Recommended certification: Databricks Data Engineering certification.
- Mastery of one platform significantly improves employability.
Build a Data Engineering Project
- Apply all learned skills in a real project.
- Options: build a data warehouse or a data lakehouse.
- Barra provides a step-by-step YouTube project on building a data warehouse with SQL.
- Building a portfolio project is crucial for demonstrating skills to employers.

Estimated learning time: 1 to 1.5 years (up to 20 months max), depending on individual pace.

Phase 2: Get Hired

Build a strong CV
- Keep it to one page.
- Highlight 1-2 technical projects relevant to data engineering.
- Tell a clear data engineer story.
Optimize LinkedIn profile
- Use a professional photo.
- Craft a strong header.
- Pin GitHub and projects prominently.
- Post regularly (weekly) about data engineering topics.
Create a strong GitHub portfolio
- 2-3 well-organized repositories showcasing skills and projects.
- Include clean code, comments, and detailed README files.
Job search mindset
- Understand job search can be random and emotional.
- Rejections are common and not personal.
- Focus on what you can control: keep applying, updating CV/profile, and learning.

Phase 3: Level Up (Professional Growth)

Cloud Skills
- Learn fundamentals of one cloud provider: Azure (recommended for Europe), AWS, or GCP.
CI/CD (Continuous Integration/Continuous Deployment)
- Learn how to move code from development to production.
- Tools: GitHub Actions, Azure DevOps, GitLab CI.
Apache Kafka
- Understand basics of streaming data.
- Learn Kafka concepts: producers, consumers (data engineers are consumers).
Advanced Data Engineering Concepts
- Data architectures: data warehouse vs. data lake vs. lakehouse.
- Data processing: batch vs. stream processing, incremental vs. full loads, slowly changing dimensions.
- Data modeling: dimensional modeling, data vault, star schema, snowflake schema.
- Performance optimization: data partitioning, cost reduction.
- Logging and monitoring pipelines for reliability.
These skills move you from junior to senior data engineer and possibly data architect.

Final Advice

The path is clear: start with SQL, Python, PySpark → learn pipelines → master a data platform → build projects → apply for jobs.
Advanced skills come with experience on the job.
Stay motivated and keep learning consistently.
Start now, don’t delay.

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Speakers/Sources Featured

Barra – Data engineer, master’s degree holder, over 10 years of experience, worked at Mercedes-Benz, creator of the video and associated courses.

This summary captures the key lessons, methodology, and advice Barra shares for becoming a data engineer in 2025, emphasizing a focused, phased approach to learning and career development.