🔑 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.
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. |
Data Career Checklist Series DataViz Explorer Page 1 of 1
DataViz Explorer C.A.I.P.O Barbados Business Registration №87900®