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Tier 2 · mid · representative cell

Data Engineer — AI labor economics

Designs and maintains data pipelines, warehouses, and infrastructure. Owns reliability, schema design, and orchestration for the data platform that analytics and ML teams depend on.

What follows is the matrix-derived read for this role at a representative cell. Your numbers will differ — compute your own Wagecard for the precise read on your geo, experience, salary, and task mix.

Operational AI cost
$6,319/mo
Tokens · oversight · retries · integration · orchestration.
Market rate (Tier 2 mid)
$139,400/yr
p25 $115,702 · p75 $164,492 · p90 $195,160
Economic substitution exposure
43 / 100
Lower = more human-leveraged. Mixed / Low confidence.
Substitution distribution
By hours-weighted share across this role's tasks
Replaceable0%
AI-augmented33%
Human-led + AI-assisted33%
Human-critical33%

Per-task read

The capability and human-advantage scores driving the role-level numbers.

Task
Capability
Reliability
Error cost
Human-advantage
Build ETL/ELT pipelines
78
70
3/5
30/100
Schema design
55
50
4/5
55/100
Pipeline debugging
50
45
4/5
65/100
Write SQL transformations
82
78
2/5
25/100
Data infrastructure architecture
40
40
5/5
75/100
Stakeholder pipeline reviews
25
25
3/5
75/100

Compute your own Data Engineer Wagecard

Pick your geo, experience, salary, and the task mix that fits your actual week. Anonymous preview before sign-in; full result + share link with a free account.

Get my Wagecard →Live insights across geo × experienceSee methodology

Computed against capability matrix v1 · model v1-mvp · representative cell (Tier 2 / mid). Refreshed when the matrix refreshes. Open methodology at /methodology.