DataViz Explorer

🔑 Free Exclusive Career Checklist:
Beginner Data Engineer

This checklist outlines the initial steps to build foundational skills and start your career in Data Engineering and Pipeline Development.

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1. Core Programming & Database Skills

Step	Action Item	Status
1.1	Master Advanced SQL: Become proficient in writing complex DDL, DML, and stored procedures for data warehousing (e.g., creating views, indexing).
1.2	Develop Python Proficiency: Master Python for scripting, I/O operations (file reading/writing), and connecting to APIs to extract data.
1.3	Understand Data Modeling: Learn and apply concepts of Dimensional Modeling (Star and Snowflake schemas).
1.4	Learn Version Control (Git): Practice standard Git workflows (clone, branch, commit, push, merge) for collaborative code development.
1.5	Build a Local Pipeline: Create a simple ETL pipeline using Python to extract data from a flat file, clean it, and load it into a local PostgreSQL or SQLite database. (Crucial)

2. Tooling & Cloud Exposure

Step	Action Item	Status
2.1	Cloud Storage Setup: Create a free-tier account on AWS, GCP, or Azure and learn to upload/manage data in object storage (S3, GCS, Blob Storage).
2.2	Data Warehouse Basics: Run basic queries on a cloud data warehouse (e.g., Snowflake, BigQuery) to understand columnar storage and query costs.
2.3	Orchestration Concept: Understand the purpose of orchestrators like Airflow and list the key components (DAGs, tasks, scheduling).
2.4	Containers (Docker): Set up Docker and containerize your local Python ETL pipeline, making it reproducible.
2.5	Data Quality/Monitoring: Learn basic Data Quality concepts (freshness, completeness, validity) and implement simple checks within your pipeline script.

3. Portfolio & Career Kickoff

Step	Action Item	Status
3.1	Create a GitHub Portfolio: Host your working pipeline code and data modeling documentation on GitHub, ensuring clean READMEs.
3.2	Focus on Pipeline Resilience: Modify your portfolio project to include basic error handling (try/except blocks) and logging.
3.3	Update Resume Keywords: Use terms like ETL/ELT, Dimensional Modeling, Cloud Storage, and **Python Scripting** to target entry-level roles.
3.4	Connect with Professionals: Identify 5-10 Data Engineers on LinkedIn and ask brief, respectful questions about their daily tech stack.
3.5	Prepare for SQL/Python Screening: Practice solving 10-15 intermediate-level SQL and Python coding challenges common in first-round interviews.