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.

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)

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.

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.

Data Career Checklist Series DataViz Explorer Page 1 of 1

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