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.
Assuming typical duties, AI may help draft pipeline code, SQL, tests, and documentation. Humans would still decide data definitions, review changes, handle incidents, and own reliability and quality outcomes.
Translating business questions into data models, agreeing definitions with stakeholders, and debugging production issues are activities worth developing, assuming this role includes them. These involve context and accountability that code generation alone does not supply.
Based on a typical version of your role. Your actual tasks may differ. 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
Assumed typical duties mix repeatable digital work, such as boilerplate code and queries, with context-heavy work like data modeling, incident response, and stakeholder alignment; no actual tasks were supplied.
20% of the total
This is a general capability estimate from model knowledge, not current evidence; AI plausibly assists with code and documentation, but pace and reliability for this work are unclear.
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
Assuming typical duties, the role can require accountability for data quality, clarifying ambiguous requirements with stakeholders, and judgment during production incidents.
10% of the total
Assuming typical duties, SQL, data modeling, and systems debugging overlap with analytics engineering, platform, and backend work; no skills were supplied, so proficiency is not established.
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
Assumed typical duties mix repeatable digital work, such as boilerplate code and queries, with context-heavy work like data modeling, incident response, and stakeholder alignment; no actual tasks were supplied.
20% of the total
This is a general capability estimate from model knowledge, not current evidence; AI plausibly assists with code and documentation, but pace and reliability for this work are unclear.
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
Assuming typical duties, the role can require accountability for data quality, clarifying ambiguous requirements with stakeholders, and judgment during production incidents.
10% of the total
Assuming typical duties, SQL, data modeling, and systems debugging overlap with analytics engineering, platform, and backend work; no skills were supplied, so proficiency is not established.
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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