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Data Coordinator
Based on 10 assessments · 1 from real users
32%
Moderate risk
Average realistic automation risk across all Data Coordinator profiles in the dataset.
Score spread
Distribution across 10 profiles.
Middle half of Data Coordinators score between 28% and 36%.
0%
50%
100%
Task breakdown by work type
Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.
Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.
Computer input, real-world output — needs someone to act on it, not just software.
No computer required. Furthest from automation — the strongest human advantage.
Typical tasks
3 synthetic profiles for a Data Coordinator, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
Cleaning and validating raw data from multiple sources (spreadsheets, databases, APIs) to ensure accuracy and consistency before analysis
deep expertise
38%
DD
33%
Monitoring data pipelines and dashboards for anomalies; investigating discrepancies and communicating findings to relevant teams
some context needed
social core
18%
DA
6%
Coordinating with stakeholders (product, analytics, engineering teams) to understand data requirements and resolve data quality issues
deep expertise
social core
14%
DA
8%
Running scheduled data extraction, transformation, and loading (ETL) processes; troubleshooting failures and reprocessing data as needed
10%
DD
48%
Preparing ad-hoc data reports and exports for internal teams, executives, or clients in requested formats
some context needed
social core
9%
DD
30%
Creating and maintaining data documentation, data dictionaries, and metadata to track data lineage and definitions across systems
deep expertise
social core
9%
DD
18%
Coordinating with stakeholders (product, analytics, engineering teams) to understand data requirements and resolve data quality issues
deep expertise
social core
27%
DA
3%
Running scheduled data extraction, transformation, and loading (ETL) processes; troubleshooting failures and reprocessing data as needed
27%
DD
44%
Cleaning and validating raw data from multiple sources (spreadsheets, databases, APIs) to ensure accuracy and consistency before analysis
18%
DD
65%
Creating and maintaining data documentation, data dictionaries, and metadata to track data lineage and definitions across systems
deep expertise
social core
10%
DD
16%
Monitoring data pipelines and dashboards for anomalies; investigating discrepancies and communicating findings to relevant teams
some context needed
social core
10%
DA
13%
Preparing ad-hoc data reports and exports for internal teams, executives, or clients in requested formats
some context needed
social core
6%
DD
29%
Cleaning and validating raw data from multiple sources (spreadsheets, databases, APIs) to ensure accuracy and consistency before analysis
30%
DD
63%
Preparing ad-hoc data reports and exports for internal teams, executives, or clients in requested formats
18%
DD
54%
Running scheduled data extraction, transformation, and loading (ETL) processes; troubleshooting failures and reprocessing data as needed
14%
DD
55%
Coordinating with stakeholders (product, analytics, engineering teams) to understand data requirements and resolve data quality issues
deep expertise
social core
13%
DA
1%
Monitoring data pipelines and dashboards for anomalies; investigating discrepancies and communicating findings to relevant teams
deep expertise
social core
12%
DA
1%
Creating and maintaining data documentation, data dictionaries, and metadata to track data lineage and definitions across systems
deep expertise
social core
11%
DD
14%
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