Will AI replace data engineers?
Updated
Data engineers score 60 of 100 for AI exposure. The largest share of their task time, 72%, is work AI speeds up while they stay in charge. AI can take over 19% outright, and 9% stays with people.
- 19% AI can do it
- 72% AI helps
- 9% Needs a person
High exposure
Higher than 91% of the 923 occupations we scored.
Task by task
We graded 25 tasks, listed here from most to least exposed. AI can do 5 of them today, speeds up 18 and leaves 2 to people.
| Task | AI exposure |
|---|---|
| Document and communicate database schemas, using accepted notations. Schema documentation and diagrams can be generated from metadata with minimal review. | 70% AI can do it |
| Write and code logical and physical database descriptions, and specify identifiers of database to management system or direct others in coding descriptions. Logical and physical schema code is highly generatable from specifications with review. | 70% AI can do it |
| Develop or maintain archived procedures, procedural codes, or queries for applications. SQL procedures and queries are routinely generated and refined by AI assistants. | 70% AI can do it |
| Test programs or databases, correct errors, and make necessary modifications. AI generates tests and suggests fixes; engineer reviews results and applies changes. | 65% AI helps |
| Develop data model describing data elements and their use, following procedures and using pen, template or computer software. Data dictionaries and element descriptions can be drafted from schemas and templates. | 62% AI can do it |
| Establish and calculate optimum values for database parameters, using manuals and calculators. Documented formulas and workload metrics suit AI; a DBA verifies results. | 62% AI can do it |
| Identify and correct deviations from database development standards. AI scans code and schemas for standard deviations; engineer approves corrections. | 60% AI helps |
| Develop data models for applications, metadata tables, views or related database structures. AI generates draft models and DDL from requirements; modeler validates fit. | 55% AI helps |
| Design database applications, such as interfaces, data transfer mechanisms, global temporary tables, data partitions, and function-based indexes to enable efficient access of the generic database structure. AI writes interfaces, partitions and index code; designer validates performance impact. | 55% AI helps |
| Design databases to support business applications, ensuring system scalability, security, performance, and reliability. AI drafts schemas and reviews designs; architects take responsibility for tradeoffs and accountability. | 45% AI helps |
Show the other 15 tasks
| Task | AI exposure |
|---|---|
| Develop methods for integrating different products so they work properly together, such as customizing commercial databases to fit specific needs. AI code assistants write integration and customization code; engineers test and approve. | 45% AI helps |
| Identify and evaluate industry trends in database systems to serve as a source of information and advice for upper management. AI can research and summarise trends; advising executives needs human judgement. | 45% AI helps |
| Develop and document database architectures. AI drafts architecture documents; architect is responsible for design decisions and tradeoffs. | 40% AI helps |
| Set up database clusters, backup, or recovery processes. AI writes backup and cluster scripts and runbooks; engineer executes and verifies. | 40% AI helps |
| Identify, evaluate and recommend hardware or software technologies to achieve desired database performance. AI can compare products and benchmarks; recommendation needs context and accountability. | 40% AI helps |
| Develop database architectural strategies at the modeling, design and implementation stages to address business or industry requirements. AI can propose strategy options; architect weighs business constraints and commits. | 35% AI helps |
| Create and enforce database development standards. AI drafts standards documents; enforcement and organisational buy-in remain human. | 35% AI helps |
| Demonstrate database technical functionality, such as performance, security and reliability. AI can generate test scenarios and reports; demonstrating reliability needs hands-on work. | 35% AI helps |
| Develop load-balancing processes to eliminate down time for backup processes. AI can draft load-balancing configurations; live infrastructure work stays with engineers. | 35% AI helps |
| Plan and install upgrades of database management system software to enhance database performance. AI drafts upgrade plans and scripts; production cutover needs human sign-off. | 35% AI helps |
| Review project requests describing database user needs to estimate time and cost required to accomplish project. AI drafts estimates from request text; stakeholder context still needs people. | 35% AI helps |
| Provide technical support to junior staff or clients. AI answers routine technical questions; complex support and mentoring needs people. | 35% AI helps |
| Train users and answer questions. AI can draft training materials and answer FAQs; live teaching remains human. | 35% AI helps |
| Work as part of a project team to coordinate database development and determine project scope and limitations. Scoping and coordinating project teams depends on negotiation and live people management. | 20% Needs a person |
| Collaborate with system architects, software architects, design analysts, and others to understand business or industry requirements. Requirements work relies on live stakeholder conversations and political judgement. | 15% Needs a person |
How data engineers compare
All occupations (923)
Computer and mathematical (36)
Pay and outlook in the US
- Median pay
- $139,500 a year, May 2025
- People employed
- 67,140 in the US, May 2025
- Projected change in jobs
- +9.4% from 2024 to 2034
Skills to build
- Review AI-drafted schema code in Amazon Redshift
- Run requirement sessions with system and software architects
- Take responsibility for design sign-off for each warehouse data model
- Build Apache Airflow pipelines for warehouse data flows
- Provision warehouse infrastructure with AWS CloudFormation templates
Safer roles nearby
5 related occupations with lower scores.
Questions about data engineers and AI
Will data engineers be replaced by AI?
Partly. About 5 in 10 of a data engineer's working hours go to tasks AI can already do, mostly writing logical and physical schema code and building SQL procedures and queries. What stays with people is gathering requirements from stakeholders, setting the scope of database projects, and signing off the design that goes live.
Which tasks is AI taking over from data engineers?
Schema documentation, logical and physical schema code, and SQL procedures and queries are the most exposed, each scoring 70 on the exposure scale. AI can generate schema diagrams from metadata and draft SQL queries from specifications, so the engineer's work moves to reviewing and correcting that output.
Which tasks is AI taking over from data engineers?
Schema documentation, logical and physical schema code, and SQL procedures and queries are the most exposed, each scoring 70 on the exposure scale. AI can generate schema diagrams from metadata and draft SQL from specifications, so the engineer's work moves to reviewing and correcting that output.
Which tasks stay with data engineers?
Collaborating with system and software architects to pin down business requirements, and coordinating project teams to set database scope, are the least exposed, scoring 15 and 20. Both depend on live stakeholder conversations and negotiation that need a person present.
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