In any profession, it is very important to stay sharp and up-to-date, and data engineering is no exception. Given today’s realities, we need to ride the AI wave, not just in our day-to-day activities - like writing or debugging code or writing documentation (tools like Cursor, ChatGPT, or Amazon Q are very helpful for this). But what about integrating modern AI into the data pipelines themselves? What projects are relatively easy for a data engineer to start with?
With platforms like Snowflake, Databricks, and AWS, data engineers can now build AI-enhanced data pipelines that don’t just move or clean data - they understand it, summarize it, and act on it. Paradigms such as RAG, AI Agents, MCP and Vector Search open opportunities to embed intelligence directly into the data platform layer.
Data Quality Monitoring with AI Agents
Traditional data quality checks rely on static rules that quickly become outdated. An AI-enhanced approach uses machine learning to detect anomalies, missing patterns, or schema drifts automatically.
Approach
Ingestion & Storage: All operational and transactional data flows into a central data lake on AWS S3 or Delta Lake.
Profiling & Feature Extraction: Detect schema changes in near real-time. Profile statistics - record counts, null percentages, ranges - are logged to a “quality metrics” table.
AI Agent layer: A small agent (built using LangChain or LlamaIndex) continuously queries these metrics. When it detects anomalies, it generates a summary and sends alerts via Slack or email.
RAG Integration: The agent can reference historical incidents and operational runbooks stored as embeddings in a vector store. It retrieves context (e“this anomaly has occurred before after a schema change”) before responding.
AI-Driven Metadata Search and Catalog Enrichment (Vector Search + MCP)
Data catalogs often struggle to keep metadata fresh and meaningful. An AI-driven metadata pipeline can automatically enrich and semantically organize datasets using embeddings and multi-component pipelines (MCP).
Approach
Metadata Extraction: Extract schema, table descriptions, and lineage data from Snowflake’s INFORMATION_SCHEMA or Databricks Unity Catalog.
Embedding Creation: Text metadata (names, comments, column descriptions) is embedded using Snowflake Cortex or Databricks Mosaic AI to produce vector representations.
Vector Search Index: These embeddings are stored in a Snowflake VECTOR column or an external vector database.
MCP Integration: Using Snowflake’s Managed MCP Servers, an AI agent can query this metadata index. For example, a user can ask:
“Which datasets contain both revenue and region information for Q4?”
The MCP agent retrieves relevant schema embeddings, runs SQL queries, and returns a structured response.Governance: The system enforces role-based access and logs all AI queries, ensuring compliance and lineage tracking.
RAG-Powered Analytics Assistant
Business analysts frequently ask repetitive questions about KPIs - revenue trends, churn rates, or supply chain delays. Instead of manually crafting SQL queries, a Retrieval-Augmented Generation (RAG) assistant can allow natural language analytics directly over governed data.
Approach
Data Layer: KPI data (sales, customer, operations) is stored in Snowflake or Databricks Gold tables, updated daily.
Semantic Index: Summaries of metric definitions, dashboards, and historical insights are embedded into a vector index using Snowflake’s EMBED_TEXT or Databricks Mosaic AI.
RAG Workflow:
User asks a question in natural language (“What regions had declining revenue last quarter?”).
The system retrieves relevant metric descriptions and definitions from the vector index.
The LLM (hosted in AWS Bedrock or SageMaker) generates the corresponding SQL query.
Snowflake Cortex Analyst executes it and returns a natural language answer, optionally with a visualization.
AI Agent Orchestration: The assistant is an MCP-based agent that chains tasks: retrieve → query → summarize → notify.
Customer Behavior Segmentation with Vector Similarity
Across industries - retail, telecom, fintech - understanding customer behavior is key. Traditional segmentation uses manual rules (“high spenders,” “frequent visitors”), but embedding-based segmentation can group customers by behavioral similarity discovered through AI.
Approach
Feature Extraction: Use Databricks to aggregate behavioral signals - transactions, session durations, product interactions - into a user-feature table.
Vectorization: Convert each customer’s profile into a vector embedding using an LLM embedding model (OpenAI’s text-embedding-3-large or Snowflake Cortex embeddings).
Similarity Computation: Store vectors in Snowflake VECTOR columns and compute pairwise cosine similarity. Cluster similar vectors (K-Means or DBSCAN in Databricks).
Business Integration: Mark clusters as interpretable segments (“price-sensitive users,” “loyal subscribers”). These segments feed into CRM or recommendation systems.
AI-Assisted Root Cause Analysis for Data Incidents
When data pipelines fail or produce unexpected results, diagnosing the root cause can take hours. An AI agent can accelerate troubleshooting by analyzing logs, lineage, and historical issues.
Approach
Event Capture: Pipeline logs (Airflow, dbt, Databricks jobs) are centralized in S3 and ingested into Snowflake.
Vectorized Knowledge Base: Log messages, error summaries, and runbooks are embedded and stored in a vector search index (Snowflake Cortex or OpenSearch).
RAG Agent: When a new failure occurs, an AI agent retrieves similar incidents and past resolutions. For example:
“Job X failed due to missing partition columns - similar to incident #321 resolved by schema fix.”MCP Orchestration: Using Snowflake’s Managed MCP, the agent can trigger operational queries - like checking table freshness or re-running dbt models - without human intervention.
Data-Driven Knowledge Base for Teams (Enterprise RAG)
Enterprise documentation - SQL playbooks, API references, ETL guides - often sits scattered across Confluence, Git, or internal wikis. Building an AI-searchable enterprise knowledge base allows teams to query this information naturally and contextually.
Approach
Document Ingestion: Use Databricks workflows or AWS Glue to crawl unstructured content (Markdown, PDFs, notebooks).
Preprocessing & Storage: Store text in Snowflake external tables or Delta Lake.
Vector Index: Embed each document chunk using Snowflake’s EMBED_TEXT or Hugging Face models and store vectors in Snowflake or OpenSearch.
RAG Pipeline:
User asks: “How do we onboard new data sources into the ETL pipeline?”
System retrieves relevant docs (based on embeddings), passes them to an LLM.
The LLM summarizes or generates a direct, citation-rich answer.
Access Control: Each document inherits its security model from the data source - ensuring compliance.
Why These Projects Matter
AI isn’t just for data scientists anymore - it’s for data platforms. By integrating intelligence at the data-engineering layer, teams can:
Accelerate Insights: Automate analysis and interpretation steps that once required manual querying.
Enhance Governance: Use semantic understanding (via embeddings and MCP) to make metadata, lineage, and policy enforcement smarter.
Reduce Operational Cost: Intelligent monitoring and AI-driven automation detect issues faster and minimize downtime.
Democratize Data Access: RAG and conversational agents let non-technical stakeholders explore data without knowing SQL.
Future-Proof Architecture: By adopting modular AI services (Snowflake Cortex, Databricks Mosaic AI, AWS Bedrock), organizations can flexibly integrate new models and tools.
Architectural Considerations
When adding AI to your data engineering stack, consider these architectural principles:
Decouple Compute and Intelligence: Keep AI services (LLMs, embeddings, agents) modular so they can evolve independently of your ETL workflows.
Use Vector Search for Context: Embedding everything - metadata, metrics, documents - enables semantic retrieval and richer automation.
Prioritize Governance: Apply the same lineage and access controls to AI-generated queries as to traditional SQL. Snowflake’s MCP and Databricks Unity Catalog both support fine-grained control.
Enable Observability: Log every AI query and response, measure accuracy and latency, and monitor model drift.
Design for Human-in-the-Loop: AI agents should recommend or assist, not silently take irreversible actions, especially in production data pipelines.
Conclusion
Data engineers today are no longer just pipeline builders - they’re AI platform architects. With the convergence of data and intelligence in tools like Snowflake Cortex, Databricks Mosaic AI, and AWS Bedrock, teams can embed AI directly where their data lives.
Whether you’re building smart data quality monitors, semantic catalogs, AI-powered analytics assistants, or RAG-driven knowledge bases, the pattern is the same: combine trustworthy data pipelines with retrieval, reasoning, and automation.
The result is a modern data platform that not only moves data efficiently - but understands and acts on it intelligently.