Distribution across 41 profiles.
Middle half of Data Engineers score between 39% and 47%.
0%
50%
100%
p10 · 37%
53% · p90
Task breakdown by work type
On-screen work89%
Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.
In-person + screen11%
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-person0%
No computer required. Furthest from automation — the strongest human advantage.
Typical tasks
3 synthetic profiles for a Data Engineer, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
TaskTimeTypeExposure
Designing and maintaining data pipelines that extract, transform, and load (ETL/ELT) data from various sources into data warehouses or lakes (e.g., using tools like Apache Airflow, Spark, or cloud services like AWS Glue).
deep expertisesocial element
30%DD
30%
Monitoring data pipeline performance, troubleshooting failures, and ensuring data quality and consistency (e.g., setting up alerts, logging, and validation checks).
deep expertisesocial element
22%DD
27%
Ensuring compliance with data governance policies, security standards, and privacy regulations (e.g., GDPR, HIPAA) in data handling and storage.
deep expertisesocial element
17%DD
32%
Implementing and managing data storage solutions (e.g., databases like PostgreSQL, cloud storage like S3, or data warehouses like Snowflake/BigQuery).
11%DD
65%
Researching and adopting new tools or technologies to improve data infrastructure efficiency, scalability, or cost-effectiveness.
7%DD
42%
Writing and optimizing SQL queries or scripts (e.g., Python, Scala) to clean, aggregate, and transform raw data into structured formats for analysis or reporting.
7%DD
62%
Collaborating with data scientists, analysts, or business teams to understand their data needs and translating those into technical requirements (e.g., schema design, data models).
some context neededsocial core
1%AD
17%
Documenting data pipelines, schemas, and processes to ensure maintainability and knowledge sharing across the team.
deep expertisesocial element
1%DD
31%
TaskTimeTypeExposure
Designing and maintaining data pipelines that extract, transform, and load (ETL/ELT) data from various sources into data warehouses or lakes (e.g., using tools like Apache Airflow, Spark, or cloud services like AWS Glue).
28%DD
63%
Writing and optimizing SQL queries or scripts (e.g., Python, Scala) to clean, aggregate, and transform raw data into structured formats for analysis or reporting.
27%DD
48%
Monitoring data pipeline performance, troubleshooting failures, and ensuring data quality and consistency (e.g., setting up alerts, logging, and validation checks).
deep expertisesocial element
19%DD
20%
Documenting data pipelines, schemas, and processes to ensure maintainability and knowledge sharing across the team.
deep expertisesocial element
9%DD
35%
Implementing and managing data storage solutions (e.g., databases like PostgreSQL, cloud storage like S3, or data warehouses like Snowflake/BigQuery).
5%DD
70%
Researching and adopting new tools or technologies to improve data infrastructure efficiency, scalability, or cost-effectiveness.
4%DD
55%
Ensuring compliance with data governance policies, security standards, and privacy regulations (e.g., GDPR, HIPAA) in data handling and storage.
deep expertisesocial element
4%DD
33%
Collaborating with data scientists, analysts, or business teams to understand their data needs and translating those into technical requirements (e.g., schema design, data models).
some context neededsocial core
1%AD
13%
TaskTimeTypeExposure
Designing and maintaining data pipelines that extract, transform, and load (ETL/ELT) data from various sources into data warehouses or lakes (e.g., using tools like Apache Airflow, Spark, or cloud services like AWS Glue).
32%DD
57%
Writing and optimizing SQL queries or scripts (e.g., Python, Scala) to clean, aggregate, and transform raw data into structured formats for analysis or reporting.
19%DD
95%
Collaborating with data scientists, analysts, or business teams to understand their data needs and translating those into technical requirements (e.g., schema design, data models).
deep expertisesocial core
19%AD
19%
Monitoring data pipeline performance, troubleshooting failures, and ensuring data quality and consistency (e.g., setting up alerts, logging, and validation checks).
18%DD
50%
Researching and adopting new tools or technologies to improve data infrastructure efficiency, scalability, or cost-effectiveness.
deep expertisesocial element
6%DD
27%
Documenting data pipelines, schemas, and processes to ensure maintainability and knowledge sharing across the team.
deep expertisesocial element
2%DD
34%
Implementing and managing data storage solutions (e.g., databases like PostgreSQL, cloud storage like S3, or data warehouses like Snowflake/BigQuery).
0%DD
62%
Ensuring compliance with data governance policies, security standards, and privacy regulations (e.g., GDPR, HIPAA) in data handling and storage.
deep expertisesocial element
0%DD
27%
Save & share
AI tools for this role
Tools relevant to the most automatable tasks in this profession.
