AI resilience estimate
Some parts of the work may change more than others. Start by finding where AI can help and where your judgment matters.
Higher means more resilience in this estimate. This is not a percentage chance of keeping or losing your job.
AI may speed up drafting pipeline code, SQL, and routine BI reports. Data modeling, architecture choices, and analysis still need people to set requirements, weigh tradeoffs, and answer for results.
Data modeling and architecture work can require translating business needs into design decisions and explaining tradeoffs to stakeholders. These are worth practicing, alongside reviewing AI-drafted code and queries critically.
Based on your role and 5 tasks you described. Your employer’s plans and local job market are not known.
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Read the reasoning and the six factors.
Higher means more resilience in this estimate. The weights show each factor’s share of the total. A score of 50 can mean information is unknown; read the note alongside it.
35% of the total
Pipeline code, queries, and routine reports are repeatable digital outputs AI can assist with, while modeling, architecture, and analysis tasks can require human context and accountability; assistance with tasks is not replacement of the whole role.
20% of the total
This is a general capability estimate from model knowledge, not a measurement of current research; code and query generation are areas where AI tools are generally capable, so the score is moderate-to-low.
15% of the total
Actual AI adoption at your employer was not checked. This neutral placeholder is unknown context, not a measured adoption rate.
15% of the total
Architecture and modeling work can require stakeholder alignment, tradeoff decisions, and accountability for data quality; these are role requirements, not demonstrated abilities of this person.
10% of the total
Data modeling, architecture, and analysis overlap with analytics engineering, BI development, and data platform work; no skills were supplied, so proficiency is unknown and this reflects task overlap only.
5% of the total
Current hiring demand was not checked. This neutral placeholder is unknown context, not evidence of average demand.
Higher means more resilience in this estimate. The weights show each factor’s share of the total. A score of 50 can mean information is unknown; read the note alongside it.
35% of the total
Pipeline code, queries, and routine reports are repeatable digital outputs AI can assist with, while modeling, architecture, and analysis tasks can require human context and accountability; assistance with tasks is not replacement of the whole role.
20% of the total
This is a general capability estimate from model knowledge, not a measurement of current research; code and query generation are areas where AI tools are generally capable, so the score is moderate-to-low.
15% of the total
Actual AI adoption at your employer was not checked. This neutral placeholder is unknown context, not a measured adoption rate.
15% of the total
Architecture and modeling work can require stakeholder alignment, tradeoff decisions, and accountability for data quality; these are role requirements, not demonstrated abilities of this person.
10% of the total
Data modeling, architecture, and analysis overlap with analytics engineering, BI development, and data platform work; no skills were supplied, so proficiency is unknown and this reflects task overlap only.
5% of the total
Current hiring demand was not checked. This neutral placeholder is unknown context, not evidence of average demand.
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