Distribution across 38 profiles.
Middle half of Analytics Engineers score between 46% and 51%.
0%
50%
100%
p10 · 41%
56% · p90
Task breakdown by work type
On-screen work75%
Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.
In-person + screen19%
Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.
Computer + action0%
Computer input, real-world output — needs someone to act on it, not just software.
Fully in-person6%
No computer required. Furthest from automation — the strongest human advantage.
Typical tasks
3 synthetic profiles for a Analytics Engineer, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
TaskTimeTypeExposure
Collaborate with data scientists and analysts to understand requirements and translate business questions into technical data solutions (e.g., defining metrics, creating data models, or optimizing queries)
deep expertisesocial core
30%AD
4%
Monitor and maintain data pipelines to ensure reliability, performance, and data quality (e.g., debugging failures, optimizing slow queries, or setting up alerts for anomalies)
20%DD
66%
Document data assets, including schemas, transformations, and business logic, to ensure clarity and reproducibility for other team members
18%DD
49%
Design and build data transformation pipelines (e.g., writing SQL or Python code to clean, aggregate, and model raw data into structured datasets for analysis)
deep expertise
17%DD
40%
Implement and manage version control for data models and pipelines (e.g., using Git or dbt) to track changes and enable collaboration
10%DD
70%
Train or mentor team members on best practices for data modeling, pipeline development, or tool usage (e.g., conducting workshops or code reviews)
deep expertisesocial element
1%AA
0%
Optimize data warehouse performance (e.g., partitioning tables, clustering, or indexing) to reduce costs and improve query speed
0%DD
64%
TaskTimeTypeExposure
Design and build data transformation pipelines (e.g., writing SQL or Python code to clean, aggregate, and model raw data into structured datasets for analysis)
25%DD
62%
Collaborate with data scientists and analysts to understand requirements and translate business questions into technical data solutions (e.g., defining metrics, creating data models, or optimizing queries)
deep expertisesocial core
23%AD
13%
Document data assets, including schemas, transformations, and business logic, to ensure clarity and reproducibility for other team members
20%DD
51%
Monitor and maintain data pipelines to ensure reliability, performance, and data quality (e.g., debugging failures, optimizing slow queries, or setting up alerts for anomalies)
14%DD
65%
Optimize data warehouse performance (e.g., partitioning tables, clustering, or indexing) to reduce costs and improve query speed
12%DD
50%
Implement and manage version control for data models and pipelines (e.g., using Git or dbt) to track changes and enable collaboration
2%DD
66%
Train or mentor team members on best practices for data modeling, pipeline development, or tool usage (e.g., conducting workshops or code reviews)
deep expertisesocial core
1%AA
12%
TaskTimeTypeExposure
Design and build data transformation pipelines (e.g., writing SQL or Python code to clean, aggregate, and model raw data into structured datasets for analysis)
34%DD
94%
Collaborate with data scientists and analysts to understand requirements and translate business questions into technical data solutions (e.g., defining metrics, creating data models, or optimizing queries)
deep expertisesocial core
17%AD
7%
Monitor and maintain data pipelines to ensure reliability, performance, and data quality (e.g., debugging failures, optimizing slow queries, or setting up alerts for anomalies)
15%DD
61%
Optimize data warehouse performance (e.g., partitioning tables, clustering, or indexing) to reduce costs and improve query speed
13%DD
68%
Implement and manage version control for data models and pipelines (e.g., using Git or dbt) to track changes and enable collaboration
10%DD
74%
Document data assets, including schemas, transformations, and business logic, to ensure clarity and reproducibility for other team members
8%DD
58%
Train or mentor team members on best practices for data modeling, pipeline development, or tool usage (e.g., conducting workshops or code reviews)
some context neededsocial core
1%AA
6%
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AI tools for this role
Tools relevant to the most automatable tasks in this profession.
