Explore KSACs by Pathway

In late 2025, the Partnership pulled together members of industry from across tech to share how the in-demand skillsets for entry-level roles in their fields have changed in the last two years. The Knowledge, Skills & Abilities and Credentials (KSACs) below reflect their feedback on entry-level expectations, especially in a tech workplace increasingly shaped by AI.

Previously two separate KSA lists, Data Management and Analytics have been combined as of 2026. This is in direct response to feedback from members of industry, who shared that with the onset of generative AI, most entry-level data roles will require literacy in skillsets across both of these two specializations.

3. Data Modeling and Design

Download the Data - PDF
Download the Data - Excel
Label KSAC Description Bloom's Taxonomy Level
a Describe the systems development life cycle (SDLC) and its relevance to data and analytics systems knowledge 2
b Explain the purpose of data modeling in analytics systems, including how models translate business requirements into technical structures knowledge 2
c Identify relational, NoSQL, and analytical data models and the types of use cases each supports knowledge 2
d Explain conceptual, logical, and physical data models and how they are used at different stages of system and analytics design knowledge 3
e Identify core data modeling components (entities, attributes, relationships, keys) and how they define data structure and integrity knowledge 3
f Describe normalization and denormalization, including their benefits and tradeoffs for analytics and performance knowledge 2
g Explain dimensional modeling concepts (facts and dimensions) and their role in analytical querying and reporting knowledge 3
h Differentiate common warehouse schemas (e.g., star and snowflake) and their implications for usability and performance knowledge 3
i Review AI-generated schemas for accuracy and completeness against defined business and analytical requirements, and flag potential concerns for escalation skill 3
j Identify common data modeling issues in automated outputs, such as inappropriate keys, incorrect relationships, or misleading abstractions, when compared to expected patterns ability 3

Credentials

Vendor Certifications - DBMS

  1. Oracle Associate
  2. Microsoft
  3. IBM

Vendor Certifications - Cloud

  1. AWS
  2. Oracle
  3. Microsoft

Vendor Certifications - Tools

  1. Tableau
  2. SAS

Others

  1. DAMA CDMP
  2. DataCamp Certificate