Data

Modeling

—

Hard

Skills

The

List

1 – 46

Conceptual

logical

and

physical

data

modeling

Entity

relationship

(

)

modeling

Relational

modeling

normalization

–5

BCNF

)

Dimensional

modeling

(

Kimball

star

schema

)

Data

Vault

2.0

modeling

Canonical

industry

reference

models

Key

design

(

surrogate

natural

keys

)

Referential

integrity

constraint

design

Cardinality

optionality

analysis

Many

many

resolution

bridge

tables

Recursive

hierarchies

traversal

SQL

Slowly

changing

dimensions

(

SCD

0–7)

Temporal

bitemporal

modeling

Physical

storage

access

path

design

Index

design

index

selection

Partitioning

sharding

strategy

Denormalization

pre

aggregation

Query

plan

analysis

performance

tuning

Data

type

numeric

precision

selection

Document

modeling

(

embedding

referencing

)

Key

value

wide

column

modeling

Graph

data

modeling

Time

series

modeling

Geospatial

full

text

modeling

Vector

embedding

schema

design

Data

warehouse

lakehouse

architecture

OLAP

multidimensional

modeling

Semantic

layer

metrics

modeling

Data

mesh

data

product

design

Master

reference

data

modeling

Event

streaming

CDC

schema

design

Data

profiling

source

reverse

engineering

Data

quality

rule

design

Metadata

management

data

lineage

Naming

conventions

data

dictionary

standards

Privacy

security

PII

aware

schema

design

Regulatory

retention

modeling

Big

data

table

formats

file

layout

Schema

evolution

migration

design

Lakehouse

partitioning

compaction

strategy

SQL

DDL

DML

advanced

analytical

SQL

Data

modeling

tools

notation

standards

Transformation

frameworks

(

dbt

Dataform

)

Schema

code

migration

tooling

Data

contract

authoring

Diagramming

documentation

tooling

1 / 6

What

Each

Skill

Covers

Definitions

scope

Modeling

Methods

Paradigms

Conceptual

logical

and

physical

data

modeling

—

moving

design

through

three

abstraction

levels

business

entities

and

rules

then

normalized

structures

then

DDL

with

types

indexes

and

storage

clauses

Entity

relationship

modeling

—

Chen

crow

foot

and

IDEF

notation

;

entity

discovery

relationship

verbs

cardinality

and

participation

constraints

Relational

modeling

normalization

—

through

and

BCNF

functional

and

multivalued

dependency

analysis

lossless

decomposition

and

deliberate

denormalization

Dimensional

modeling

(

Kimball

)

—

grain

declaration

fact

table

types

conformed

dimensions

bus

matrix

design

snowflaking

trade

offs

junk

degenerate

and

role

playing

dimensions

Data

Vault

2.0

modeling

—

hubs

links

satellites

hash

keys

raw

vault

versus

business

vault

and

auditable

insert

only

historization

Canonical

industry

reference

models

—

adapting

IBM

BDW

FSLDM

ACORD

FHIR

and

Forum

SID

structures

specific

enterprise

Keys

Relationships

Integrity

Key

design

—

surrogate

versus

natural

keys

composite

keys

candidate

and

alternate

keys

UUID

ULID

generation

and

hash

key

construction

Referential

integrity

constraint

design

—

primary

and

foreign

keys

unique

check

not

null

and

default

constraints

plus

delete

and

update

cascade

rules

Cardinality

optionality

analysis

—

resolving

1:1, 1:

and

relationships

and

documenting

minimum

and

maximum

participation

each

side

Many

many

resolution

bridge

tables

—

associative

entities

weighting

factors

multi

valued

dimension

bridging

and

factless

fact

tables

Recursive

hierarchies

traversal

SQL

—

adjacency

lists

path

enumeration

nested

sets

closure

tables

and

recursive

CTEs

for

ragged

and

variable

depth

trees

Slowly

changing

dimensions

(

SCD

0–7)

—

Type

overwrite

through

Type

hybrid

Type

dual

key

effective

dating

current

flag

columns

and

late

arriving

dimensions

Temporal

bitemporal

modeling

—

valid

time

versus

transaction

time

system

versioned

tables

time

travel

queries

and

reporting

2 / 6

Physical

Design

Performance

Physical

storage

access

path

design

—

tablespaces

and

filegroups

row

versus

column

store

page

and

row

structure

compression

and

tablespace

placement

Index

design

selection

—

clustered

and

nonclustered

tree

bitmap

covering

filtered

hash

GIN

GiST

and

composite

key

order

decisions

Partitioning

sharding

strategy

—

range

list

and

hash

partitioning

partition

pruning

distribution

keys

and

shard

key

selection

for

even

load

Denormalization

pre

aggregation

—

materialized

views

summary

and

rollup

tables

aggregate

navigation

and

read

model

projections

Query

plan

analysis

performance

tuning

—

execution

plans

cardinality

estimation

statistics

maintenance

join

strategy

selection

and

scan

versus

seek

diagnosis

Data

type

precision

selection

—

DECIMAL

versus

float

date

and

timezone

handling

CHAR

VARCHAR

sizing

LOB

storage

and

semi

structured

types

(

JSON

VARIANT

Non

Relational

Specialized

Models

Document

modeling

—

embedding

versus

referencing

aggregate

boundaries

bounded

array

growth

and

schema

versioning

MongoDB

class

stores

Key

value

wide

column

modeling

—

access

pattern

first

table

design

partition

and

clustering

keys

denormalized

query

tables

and

hot

partition

avoidance

Cassandra

and

DynamoDB

Graph

data

modeling

—

property

graph

and

RDF

triples

node

and

relationship

labels

supernodes

traversal

patterns

and

relationship

direction

choices

Time

series

modeling

—

tags

versus

fields

hypertable

and

chunk

design

retention

policies

continuous

aggregates

and

downsampling

tiers

Geospatial

full

text

modeling

—

geometry

and

geography

types

spatial

index

selection

inverted

indexes

analyzers

and

relevance

field

design

Vector

embedding

schema

design

—

vector

columns

dimensionality

and

metric

choices

approximate

nearest

neighbor

indexes

and

hybrid

metadata

filtering

3 / 6

Analytics

Warehousing

Lakehouse

Data

warehouse

lakehouse

architecture

—

Inmon

CIF

versus

Kimball

raw

and

conformed

zones

medallion

bronze

silver

gold

layering

and

lake

warehouse

boundaries

OLAP

multidimensional

modeling

—

cubes

MOLAP

ROLAP

HOLAP

trade

offs

aggregation

levels

drill

paths

and

attribute

hierarchies

Semantic

layer

metrics

modeling

—

defining

certified

metrics

dimensions

and

joins

once

every

tool

reports

the

same

number

Data

mesh

data

product

design

—

domain

ownership

boundaries

input

and

output

ports

discoverable

products

and

federated

computational

governance

Master

reference

data

modeling

—

golden

record

construction

match

and

merge

survivorship

rules

cross

reference

tables

and

entity

hierarchies

Event

streaming

CDC

schema

design

—

event

envelopes

Kafka

key

and

partition

strategy

Avro

and

Protobuf

contracts

schema

registry

usage

and

idempotent

replay

Quality

Governance

Standards

Data

profiling

source

reverse

engineering

—

null

and

distinct

value

analysis

value

distribution

uniqueness

checks

functional

dependency

discovery

and

legacy

schema

reconstruction

Data

quality

rule

design

—

expressing

completeness

accuracy

consistency

and

timeliness

testable

declarative

constraints

Metadata

management

data

lineage

—

business

and

technical

metadata

column

level

lineage

catalog

registration

and

impact

analysis

for

schema

changes

Naming

conventions

data

dictionary

standards

—

consistent

entity

column

and

abbreviation

rules

;

documented

definitions

;

and

maintained

business

glossary

Privacy

security

PII

aware

schema

design

—

PII

classification

tokenization

and

masking

columns

row

and

column

level

security

and

encryption

boundaries

Regulatory

retention

modeling

—

GDPR

and

CCPA

erasure

and

retention

requirements

HIPAA

field

handling

PCI

DSS

scope

reduction

and

audit

trail

design

4 / 6

Big

Data

Modern

Storage

Big

data

table

formats

file

layout

—

Parquet

ORC

and

Avro

encoding

;

Delta

Lake

Apache

Iceberg

and

Hudi

table

semantics

;

and

predicate

pushdown

design

Schema

evolution

migration

design

—

expand

and

contract

migrations

backward

compatible

column

additions

versioned

contracts

and

zero

downtime

cutover

Lakehouse

partitioning

compaction

strategy

—

partition

column

choice

file

size

targeting

small

file

compaction

ordering

and

manifest

pruning

Tooling

Languages

SQL

DDL

DML

advanced

analytical

SQL

—

CREATE

and

ALTER

statement

design

MERGE

upserts

window

functions

CTEs

and

dialect

specific

analytic

constructs

Data

modeling

tools

notation

standards

—

hands

work

Erwin

Studio

PowerDesigner

and

SqlDBM

including

forward

and

reverse

engineering

Transformation

frameworks

—

dbt

and

Dataform

model

layering

sources

refs

incremental

materializations

and

Spark

DataFrame

transformations

Schema

code

migration

tooling

—

Git

versioned

DDL

Flyway

Liquibase

and

Alembic

changelogs

review

workflow

and

rollback

planning

Data

contract

authoring

—

publishing

schema

semantics

SLAs

and

ownership

for

dataset

producers

and

consumers

agree

before

changes

ship

Diagramming

documentation

tooling

—

producing

reviewable

ERDs

and

data

flow

diagrams

dbdiagram

draw

Lucidchart

Mermaid

PlantUML

5 / 6

Target

Proficiency

Profile

Senior

data

modeler

DOMAIN

SKILLS

TARGET

LEVEL

RANGE

Modeling

methods

paradigms

Expert

01–06

Keys

relationships

integrity

Expert

07–13

Physical

design

performance

Advanced

14–19

Non

relational

specialized

models

Advanced

20–25

Analytics

warehousing

lakehouse

Expert

26–31

Quality

governance

standards

Advanced

32–37

Big

data

modern

storage

Advanced

38–40

Tooling

languages

Expert

41–46

6 / 6

Data Modeling Hard Skills

Prompt to Clone This Data Modeling Hard Skills

Apply this example's core structure, strategic pacing, and structural hierarchy directly to your own topic.

What are you generating today?

Accounts Receivable Hard Skills

Cost Accounting Hard Skills

Investment Analysis Hard Skills

Search Engine Optimization Hard Skills

Search Engine Marketing Hard Skills

Google Analytics Hard Skills

Email Marketing Hard Skills

Content Marketing Hard Skills

Marketing Automation Hard Skills

Market Research Hard Skills

Sales Forecasting Hard Skills

Lead Generation Hard Skills

Customer Relationship Management Hard Skills

Similar Examples