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AI Automation Index
Will AI Replace Data Warehousing Specialists?
Task-level automation exposure for Data Warehousing Specialists: 18 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
68%
weighted task index
18 of 18 tasks scored
Median Salary
$139,500 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
67,140 people hold this job — size, not growth
Projected 2025–35
+9.4%
employment change
3,900 openings a year · BLS line item
Pay by state
BLS OEWS publishes 47 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$95,620 – $170,160
Highest medians
California $170,160
Massachusetts $161,650
Virginia $160,360
Arizona $156,100
Colorado $154,560
Lowest medians
Puerto Rico $95,620
Mississippi $98,420
Arkansas $104,070
Alabama $105,450
North Dakota $109,430
State figures come from the 15-1243 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 68%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 0%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 47%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 10%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 0/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
18 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 89%
16 tasks
Medium exposure (≥ 35%) 11%
2 tasks
Low exposure (below 35%) 0%
0 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#16132 · Core · 60% automatable
Develop data warehouse process models, including sourcing, loading, transformation, and extraction.
data analysis — AI models comms systems; engineer verifies performance
Develop models of information or communications systems.
#16121 · Core · 78% automatable
Verify the structure, accuracy, or quality of warehouse data.
data analysis — Data quality profiling automated, human sets standards
Evaluate data quality.
#16124 · Core · 70% automatable
Map data between source systems, data warehouses, and data marts.
visual inspection — Generates flow charts; system accuracy verified by human
Develop diagrams or flow charts of system operation.
#16126 · Core · 72% automatable
Develop and implement data extraction procedures from other systems, such as administration, billing, or claims.
text generation — AI drafts data management procedures; human approves
Develop procedures for data management.
#16128 · Core · 75% automatable
Design and implement warehouse database structures.
text generation — Database creation largely automatable; human reviews
Create databases to store electronic data.
#16127 · Core · 72% automatable
Develop or maintain standards, such as organization, structure, or nomenclature, for the design of data warehouse elements, such as data architectures, models, tools, and databases.
text generation — AI drafts data management procedures; human approves
Develop procedures for data management.
#16118 · Core · 60% automatable
Provide or coordinate troubleshooting support for data warehouses.
decision support — AI assists diagnosis; human executes fixes
Troubleshoot issues with computer applications or systems.
#16120 · Core · 71% automatable
Write new programs or modify existing programs to meet customer requirements, using current programming languages and technologies.
text generation — Code generation strong; human reviews and integrates
Modify software programs to improve performance. Design software applications. Write computer programming code.
#16133 · Core · 72% automatable
Design, implement, or operate comprehensive data warehouse systems to balance optimization of data access with batch loading and resource utilization factors, according to customer requirements.
text generation — AI drafts data management procedures; human approves
Develop procedures for data management.
#16123 · Core · 75% automatable
Perform system analysis, data analysis or programming, using a variety of computer languages and procedures.
data analysis — Trend and relationship detection in data
Analyze data to identify trends or relationships among variables. Design software applications. Write computer programming code.
#16129 · Core · 74% automatable
Create supporting documentation, such as metadata and diagrams of entity relationships, business processes, and process flow.
text generation — Draft operational procedure documentation
Develop diagrams or flow charts of system operation. Document operational procedures.
#16131 · Core · 60% automatable
Create or implement metadata processes and frameworks.
data analysis — AI models comms systems; engineer verifies performance
Develop models of information or communications systems.
#16117 · Core · 55% automatable
Review designs, codes, test plans, or documentation to ensure quality.
decision support — Design feasibility review aided, engineer owns judgment
Evaluate project designs to determine adequacy or feasibility.
#16130 · Core · 65% automatable
Create plans, test files, and scripts for data warehouse testing, ranging from unit to integration testing.
text generation — AI drafts testing routines; engineer verifies coverage
Develop testing routines or procedures.
#16122 · Core · 70% automatable
Select methods, techniques, or criteria for data warehousing evaluative procedures.
data analysis — IT performance metrics well-suited to AI
Develop performance metrics or standards related to information technology.
#16125 · Core · 50% automatable
Implement business rules via stored procedures, middleware, or other technologies.
decision support — AI advises on IT solutions; humans implement and validate
Apply new technologies to improve work processes. Apply information technology to solve business or other applied problems.
#16119 · Core · 78% automatable
Prepare functional or technical documentation for data warehouses.
text generation — Draft operational procedure documentation
Document operational procedures.
#16116 · Core · 72% automatable
Test software systems or applications for software enhancements or new products.
data analysis — AI can run and analyze software performance tests
Test software performance.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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