Overview
A brief introduction to what data engineering is and why it is important. Mention your expertise and approach to data engineering.
Skills and Tools
List the key skills and tools you use. Examples:
- ETL Pipelines (Airflow, Luigi)
- Data Warehousing (BigQuery, Snowflake, Redshift)
- Programming (Python, SQL, Spark)
- Cloud Platforms (AWS, GCP, Azure)
- Database Management (PostgreSQL, MySQL)
Featured Projects
- Project 1: [Project Title]
- **Objective**: Describe the goal of the project.
- **Tools Used**: List the technologies and tools.
- **Outcome**: Highlight key achievements or metrics (e.g., "Reduced ETL processing time by 30%").
- **Links**: Include links to code repositories, blog posts, or case studies.
- Project 2: [Another Project]
Repeat the same structure for another key project.
Tutorials and Resources
Include any content you've created or recommend:
- Tutorials on building ETL pipelines, optimizing queries, etc.
- Links to blog posts or YouTube videos you've created.
- Resources like cheat sheets, GitHub repositories, or reading materials.
Achievements
Highlight any certifications, awards, or recognition you've received related to data engineering:
- Google Cloud Professional Data Engineer
- AWS Certified Solutions Architect
- Open-source contributions
Learn More
Include links to related pages, such as:
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- 2. **Add Visual Elements**
Make the page engaging by including images, diagrams, and links: - **Images**: Use architecture diagrams of pipelines, dashboards, or tools. - **Tables**: Compare tools or summarize skills (e.g., comparison of databases). - **Code Snippets**: Add example SQL queries or Python scripts using the `<syntaxhighlight>` tag:
```sql SELECT * FROM my_table WHERE processed_date > '2024-01-01';