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In short

Of the 50 data architect roles at tech companies whose requirements we read in the 90 days to Oct 16, 2026, Data Modeling is required in 30 of 50 (60%), SQL in 24 of 50 (48%) and Data Warehousing in 19 of 50 (38%). Name the ones you have in your latest jobs, next to what you built with them.

Degree required, no alternative
17 of 48
Of the roles we could read for it

The skills these postings ask for most

Counts are out of the 50 roles whose requirements we could read; the 10 asked for most in each list.

Required
SkillRoles asking
Data Modeling
30 of 50 (60%)
SQL
24 of 50 (48%)
Data Warehousing
19 of 50 (38%)
Azure
18 of 50 (36%)
Python
18 of 50 (36%)
Data Governance
17 of 50 (34%)
Databricks
16 of 50 (32%)
Spark
16 of 50 (32%)
Data Quality
15 of 50 (30%)
ETL
15 of 50 (30%)
Preferred
SkillRoles asking
CI/CD
5 of 50 (10%)
AWS
4 of 50 (8%)
Stream Processing
4 of 50 (8%)
AI
3 of 50 (6%)
Business Intelligence
3 of 50 (6%)
Data Catalogs
3 of 50 (6%)
Data Governance
3 of 50 (6%)
Data Infrastructure
3 of 50 (6%)
Data Quality
3 of 50 (6%)
Git
3 of 50 (6%)

What to put first

  1. Lead with Data Modeling, SQL and Data Warehousing

    They are the required skills named most, with their counts in the table above. Put the ones you have in your skills line and in the bullets of your latest job, each next to what you did with it.

  2. Then CI/CD and AWS, if you have them

    Preferred, not required: CI/CD in 5 of 50 (10%) and AWS in 4 of 50 (8%). Add them where they are true; leaving one out costs less than claiming it.

  3. Make 8+ years easy to count

    Among roles that state years, 26 of 42 ask for at least 8 years. Give every job a start and end month and year, so a reader can add them up.

  4. Education can go last

    17 of 48 roles require a degree with no alternative; the rest accept experience instead, prefer one or do not mention it. Lead with your work.

What changes by level

No two neighbouring data architect levels have 50 or more roles whose requirements we read yet, so levels are not compared. The career path lists each level and its roles.

Data architect career path

Bullet patterns

Replace every bracket with your own facts. Keep only numbers you can explain in an interview.

  • Pattern, not a real resume

    Built [system] that [what it does] for [team or users], using Data Modeling; [metric] after [time].

  • Pattern, not a real resume

    Used SQL to cut [metric] from [before] to [after] by [change you made].

  • Pattern, not a real resume

    Applied Data Warehousing to [problem], which [made it cheaper, faster or more reliable] by [amount].

  • Pattern, not a real resume

    Led [project] with [number] people across [teams]; shipped [outcome] in [time].

Gaps to check before you send it

  • A required skill you use but never name

    If you work with Data Modeling and SQL but your bullets only say what the product did, a reader looking for the skill will not find it. Name it where you used it.

  • Dates that do not add up

    Most roles that state years ask for at least 8 years. Missing months, overlaps and unexplained gaps make that hard to count.

  • Bullets without an outcome

    A bullet that names a tool but not what changed reads as a list of tasks. Add what moved and by how much, or what shipped.

More on data architect roles

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How we count: 50 roles in the 90 days to Oct 16, 2026, each role counted once however many boards post it. Methodology