Over the past few years, artificial intelligence has transformed how we interact with data. From writing SQL to building pipelines, debugging models to surfacing insights, AI tools are now embedded in nearly every layer of the modern data stack. But not all AI tools serve the same purpose, or the same audience.

The rise of AI tools for analytics engineers

Today’s AI analytics landscape is split into two major camps:

  • Democratizing access to data: These tools help non-technical users get insights without writing code, often using natural language to ask questions, auto-generate dashboards, or explain anomalies. 
    The promise is compelling: reduced backlog for analytics teams, faster decision-making across the organization, and the ability for domain experts to explore data independently. However, as early users have noted, these tools still require clean, well-governed data and some level of data literacy to ask the right questions and interpret results correctly.
  • Empowering analytics engineers and data professionals: Supporting analytics engineers means solving real pain points. These are the people responsible for building and maintaining the data stack—often under pressure to deliver quickly, with limited resources, and across fragmented tools. From pipeline maintenance to model sprawl, the challenges are well known.
    AI tools can ease some of that burden. They help engineers write cleaner code, automate repetitive tasks like documentation or testing, and understand large, complex data models. That leaves more time for higher-impact work: designing better systems, improving data quality, and scaling analytics across the business.


While these paths seem distinct, they’re converging. The most effective AI tools combine both approaches, empowering business users with self-service capabilities while providing analytics engineers with powerful development tools to build and maintain the systems that make self-service possible.

How AI is reshaping data analytics

Where traditional workflows required writing every line of SQL, documenting each model by hand, and answering repetitive questions from business teams, AI now steps in to automate, assist, and scale. The role of the analytics engineer is evolving from code-writer and data steward to architect of intelligent systems.

Natural language as the new query language

AI is making natural language a viable alternative to SQL. Instead of writing complex SQL joins and aggregations, analysts can now ask: “Show me customer churn by region for enterprise clients who joined in the last year, excluding trial accounts.” The AI translates this into optimized SQL, executes it, and presents results with appropriate visualizations.

Automated code generation and optimization

AI is becoming increasingly sophisticated at generating not just queries, but entire data pipelines, transformations, and models. For example, tools like dbt Copilot can create complete analytics models from descriptions. Engineers spend less time on repetitive tasks and more time solving complex problems.

From reactive to proactive analytics

Traditional analytics has been reactive: analysts respond to questions, create reports when requested, and investigate issues after they’re discovered. AI is enabling a shift toward proactive analytics where systems automatically surface insights, predict problems before they occur, and suggest questions users should be asking.

Top 7 AI Tools for Analytics Engineers in 2025

1. Euno

AI features: Euno stitches together column-level lineage across your warehouses, dashboards, and metrics, with field-level usage to highlight what matters, transforming your metadata into a complete and actionable foundation for governance and AI. You can define custom properties to flag what meets your standards, then activate this metadata to surface real-time context to AI agents, so they deliver trusted results, every time. It also provides an AI assistant that helps analytics engineers navigate their data models using natural language, understanding relationships, lineage, and business logic across the entire data ecosystem. 

How it helps: Unlike point solutions, Euno’s AI agents have full context of your data from raw tables to final dashboards. Analytics engineers can ask questions like “Why don’t the numbers match ” and get complete lineage and code explanations. The AI assistant understands both technical implementation and business meaning, making it easier to maintain complex data models and onboard new team members.

Best for: Organizations wanting to make AI for analytics work beyond the proof of concept.

2. Tableau Pulse

AI features: Tableau has integrated generative AI through Tableau Pulse (AI-driven insights feed). Users can ask questions in natural language and receive visualizations and explanations instantly. Tableau Pulse delivers personalized, contextual insights directly into workflows, proactively suggesting questions users might not have considered.

How it helps: Reduces manual chart creation and exploratory analysis work. Analytics engineers and business users get instant answers and visualizations from natural language questions, accelerating data exploration and decision-making. The AI explains trends, identifies outliers, and surfaces hidden insights.

Best for: Organizations looking to democratize BI while maintaining professional-grade analytics capabilities.

3. Snowflake

AI features: Snowflake Cortex provides integrated access to large language models and pre-built analytical functions directly within the data warehouse. Snowflake Copilot generates and refines SQL queries from natural language prompts, while built-in functions handle sentiment analysis and anomaly detection without custom code. Snowflake Intelligence offers business users and data professionals a unified conversational experience to ask natural language questions and instantly uncover actionable insights from both structured tables and unstructured documents

How it helps: Streamlines data exploration for analytics engineers. The LLM-driven Copilot saves time by generating correct SQL without manual coding, while keeping AI and data together in one governed platform for faster development cycles.
However, it primarily operates within the confines of the data warehouse, lacking end-to-end visibility across the entire analytics stack from the data warehouse to the BI layer.

Best for: Teams wanting to perform advanced analytics directly in their data warehouse with enterprise-grade governance.

4. Databricks Genie 

AI features: Provides a chat interface where users can ask questions in natural language and receive answers backed by Databricks’ Lakehouse data. Generates visualizations and insights without requiring SQL knowledge, with continuous learning and integration with Unity Catalog for governance.

How it helps: Dramatically speeds up ad-hoc analysis and reduces reporting backlogs. Analytics engineers can rely on Genie for routine insights while focusing on complex tasks. Self-service capabilities mean fewer simple questions reach the data team. However, Databricks Genie can only query objects registered in Unity Catalog; anything transformed in a downstream BI tool is invisible.

Best for: Organizations using Databricks who want to enable self-service analytics while maintaining data governance.

5. Cursor 

AI Features: AI-powered code editor with built-in assistant for SQL, Python, dbt, and more. Can refactor code, generate functions from comments, explain errors, and act as a pair-programmer for data analytics tasks.

How it helps: Accelerates complex SQL and ETL code development by handling boilerplate and syntax. Particularly effective for specific tasks like “optimize this SQL join” or “add a dbt test for this model.” Reduces context-switching by keeping AI help within the coding environment.

Best for: Analytics engineers who want an AI coding assistant that understands data-specific workflows and tools.

6. TextQL 

AI features: Provides an AI agent that connects to multiple data sources and tools, enabling natural language interaction across all enterprise data. 

How it helps: Acts as a scalable AI assistant for data requests and analysis. Reduces repetitive analytical work, prevents dashboard sprawl by finding existing reports, and maintains consistent logic through established definitions. Enables non-technical users to get insights while ensuring governance. Limitations: These agents lack context about data assets and require a clean, managed environment to deliver value.

Best for: Enterprises seeking comprehensive AI-driven analytics across multiple data sources with strong governance requirements.

7. Dbt copilot

AI features: dbt Copilot automatically generates documentation, semantic models, and data tests. It provides visual model building with natural language prompts,and enables AI-powered querying and analysis within the dbt environment.

How it helps: Automates tedious documentation and testing tasks that improve data quality. Natural language model building bridges the gap between SQL-centric teams and business analysts. All features work within dbt’s governance framework with version control and reviews.

Best for: Analytics engineering teams using dbt who want to accelerate model development and improve documentation practices.

 

What to look for in an AI data tool

Not every AI tool is built for analytics engineers. Some focus on business users. Others promise automation but fall short in real workflows. If you’re choosing tools for your team, here’s what’s worth paying attention to.

Understands your data model

The best tools don’t just guess. They understand your tables, joins, and metrics. They work with your semantic layer, not around it. That context makes the difference between helpful suggestions and noisy output.

Works with your stack

AI is most useful when it runs close to the data—inside your warehouse or BI tool—but that’s not enough on its own. The tool also needs to understand the broader context. That means knowing how your metrics are defined, how your data is modeled, and how different layers connect. Make sure the tools you choose provide that context, not just access.

Supports the way you work

Look for tools that fit your workflow—not the other way around. If your team writes in dbt or builds with notebooks, pick tools that support that. Copilots and assistants are only useful if they show up where the work happens.

Gives you control

A good AI tool should help you go faster without removing your judgment. You should be able to review, edit, and understand what it’s doing. Tools that generate code, metrics, or dashboards should make it easy to inspect and verify.

Keeps governance intact

AI can create more risk if it’s not tied to access control, lineage, or documentation. Choose tools that respect your data permissions and make it easy to track where outputs come from. The best ones improve governance, not weaken it.

Actually saves time

This should be obvious, but it’s worth saying: if the tool takes longer to set up than the thing it replaces, it’s not helping. Test it on real workflows. If it cuts time on modeling, debugging, or reporting, it’s a good sign.

Frequently Asked Questions

How do AI tools improve analytics engineering workflows?

AI tools improve analytics engineering workflows in several key ways:

Automation of routine tasks: AI handles repetitive work like writing boilerplate SQL, generating documentation, and creating basic tests, freeing analytics engineers to focus on complex problem-solving and strategic initiatives.

Accelerated development: Tools like Cursor and dbt Copilot can generate code snippets, complete functions, and entire data models from natural language descriptions, significantly speeding up development cycles.

Enhanced code quality: AI can suggest optimizations, identify potential errors, and recommend best practices, leading to more efficient and maintainable code.

Improved documentation: AI automatically generates and maintains documentation, ensuring data models are well-documented without manual effort.

Faster troubleshooting: AI assistants can help diagnose query performance issues, explain complex code, and suggest fixes for errors.

Self-service enablement: By handling routine analytical requests through natural language interfaces, AI reduces the backlog of ad-hoc queries that analytics engineers typically manage.

What features should I look for in an AI data tool?

When selecting an AI data tool, prioritize these essential features:

Natural language query generation: The ability to translate business questions into accurate SQL or other query languages while understanding your specific data schema and business context.

Code generation and completion: Intelligent code suggestions and auto-completion that understands data engineering patterns, SQL optimization, and your organization’s coding standards.

Automated documentation: AI that can generate and maintain documentation for data models, transformations, and business logic.

Data lineage understanding: Tools that comprehend how data flows through your systems and can explain relationships and dependencies.

Multi-Tool Integration: Compatibility with your existing data stack, from warehouses to BI tools to orchestration platforms.

Which industries benefit from AI tools for analytics engineering?

While AI tools for analytics engineering provide value across all industries, certain sectors see particularly significant benefits:

Financial services: Banks, insurance companies, and fintech firms benefit from AI’s ability to handle complex regulatory reporting, real-time fraud detection, and risk analytics at scale.

Healthcare: AI tools help healthcare organizations manage vast amounts of patient data, clinical research, and operational metrics while maintaining strict privacy compliance.

E-commerce and retail: These industries leverage AI for real-time personalization, inventory optimization, and customer behavior analysis across multiple touchpoints.

Technology companies: Software and SaaS companies use AI tools to analyze user behavior, optimize product performance, and make data-driven feature decisions rapidly.

Manufacturing: AI helps optimize supply chains, predict equipment maintenance needs, and improve quality control through advanced analytics.

Media and entertainment: Content companies use AI to analyze viewer behavior, optimize content recommendations, and measure engagement across platforms.

The common thread across these industries is the need to process large volumes of data quickly, make real-time decisions, and maintain high-quality analytics despite resource constraints.

Are AI tools for analytics engineers difficult to learn?

AI tools for analytics engineers are generally designed to be intuitive and reduce learning curves rather than increase them:

Natural learning curve: Most AI tools use natural language interfaces, making them accessible to anyone who can articulate business questions clearly. The learning curve is often shorter than traditional analytics tools.

Familiar interfaces: Many AI tools integrate into existing environments (like VS Code for Cursor or existing BI platforms for Tableau GPT), minimizing the need to learn entirely new interfaces.

Built-in guidance: AI assistants often provide suggestions, explanations, and examples, making them partially self-teaching.

Reduced technical barriers: Rather than requiring users to learn complex syntax or programming languages, AI tools often handle the technical complexity behind the scenes.

However, some considerations remain:

  • Users still need to understand data concepts and business context to ask meaningful questions
  • Analytics engineers need to verify AI-generated code and understand its implications
  • Organizations should invest in training to help users get the most value from these tools

The key is that AI tools typically reduce the technical learning burden while still requiring domain knowledge and critical thinking skills.