DataViz Explorer

🔑 Free Exclusive Career Checklist:
Data Architect

This checklist outlines the initial steps to design and manage the blueprints for an organization’s entire data infrastructure, including data warehousing and cloud services.

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1. Data Modeling & Infrastructure Design

Step	Action Item	Status
1.1	Data Modeling Theory: Clearly define the differences between Conceptual, Logical, and Physical data models, and know which stakeholders use each.
1.2	Dimensional Modeling: Design a schema for a simple business scenario (e.g., E-commerce sales) using a Star or Snowflake Schema pattern.
1.3	Data Flow & Lineage: Map out the journey of data from a source application through an ETL/ELT process to a final report (data lineage).
1.4	Data Storage Types: Articulate the pros and cons of different storage solutions: Relational DB, Data Lake, and Data Warehouse.
1.5	Data Security Architecture: Design a basic access control model that uses different security layers (network, user, data encryption).

2. Cloud and Big Data Concepts

Step	Action Item	Status
2.1	Cloud Data Fundamentals: Set up and query a managed data warehouse service on one major cloud platform (e.g., AWS Redshift, Azure Synapse, or GCP BigQuery).
2.2	Data Ingestion: Use a simple ETL/ELT tool (e.g., Fivetran, Stitch, or basic Python) to move data from a source (like a CSV or API) into a destination database.
2.3	API Integration: Write code (Python/Java) to ingest data from a REST API, process the JSON/XML payload, and store it in a NoSQL database.
2.4	Data Virtualization: Understand and explain how data virtualization and data mesh concepts differ from traditional data warehousing.
2.5	Scalability Principles: Articulate the differences between horizontal and vertical scaling and why this matters for high-volume data architecture.

3. Professional Development & Portfolio

Step	Action Item	Status
3.1	Create a Data Architecture Portfolio: Document a project where you designed a data lake *and* a data warehouse to serve different business needs.
3.2	Business Requirement Translation: Practice translating a non-technical business goal (e.g., “We need faster reporting”) into technical architectural requirements.
3.3	Focus on a Cloud Certification: Begin studying for an introductory cloud data certification (e.g., Azure Data Fundamentals, AWS Certified Data Engineer Associate).
3.4	Update Resume Keywords: Use terms like Data Modeling, Dimensional Modeling, Star Schema, ETL/ELT, Cloud Data Warehousing (mentioning specific vendors), and Data Governance.
3.5	Communicate Architectural Decisions: Learn to justify a complex architectural choice (e.g., using a stream processor over batch processing) to technical and business stakeholders.