Data Engineering with AI Agents

Data Engineering with Agents

Data engineering is a unique problem to solve with AI. Unlike writing tools, email follow-ups, or even adjacent tools like AI data analysts, data engineering is primarily a write-operation field. That makes it a harder challenge because it requires deeper integration with infrastructure.

Why context matters

Take a billing management system as an example. To build it, you need to create tables in your database and define the right foreign key relationships. That sounds simple until you look at the scale. Most production databases do not have two tables — they have hundreds or thousands.

For an AI system to create or modify such a system, it must be able to parse all those tables accurately. It has to identify which tables matter, understand how they connect, and distinguish similar-sounding artifacts. It must recognize both structural details and semantics, such as separating users tables from sales tables.

This level of context is mandatory before an automation system can safely propose a change.

What happens after discovery

Even if the system can parse context correctly, it still has to apply changes without breaking downstream jobs. This is difficult because even human data engineers introduce mistakes. A migration that looks safe can block writes, corrupt data, or break pipelines.

The only way forward is to stage changes before they reach production:

• Run proposed migrations in a sandbox that mirrors your schema and includes representative data.

• Validate changes by replaying Airflow or dbt jobs against the new schema.

• Check that downstream reports, dashboards, and APIs continue to produce correct results.

• Inspect query plans to catch regressions in latency or index usage.

Only after a change is validated should it be promoted to staging and then production.

The role of lineage

Lineage is critical to this process. Schema alone cannot tell you the full impact of a change. You need to know which jobs consume each table, which columns are referenced, and which dashboards or services will be affected. Column-level lineage provides the precision needed to track changes through every downstream dependency. Without it, the system is guessing.

With lineage, the agent can:

• Identify every DAG or model that depends on a column.

• Surface impact reports when proposing a schema change.

• Generate the updates required for dependent jobs.

Safe rollout patterns

Once proposals are validated, rollout requires proven patterns:

• Expand and contract: add new columns or tables, backfill, and dual write until consumers are migrated, then remove deprecated fields.

• Dual writes: populate both old and new schema versions until downstream systems switch over.

• Blue and green cutovers: stage an entire environment in parallel and shift traffic only after verification.

These patterns minimize downtime and provide rollback paths if something fails.

Example: extending billing

Imagine you want to add discount_amount to invoices and change amount from INTEGER to DECIMAL(12,2).

1. Discovery: collect schema and lineage for invoices, payments, and dependent jobs.

2. Proposal:

   • Add amount_decimal and discount_amount.

   • Backfill amount_decimal.

   • Patch dependent jobs and dashboards.

   • Enable dual writes.

3. Sandbox: apply migration to a replica, replay DAGs and models, and compare outputs.

4. Rollout: deploy in staging, verify, and then move production traffic gradually.

5. Contract: remove legacy amount once all dependencies are migrated.

Raising the bar for agents

A credible AI data engineer must:

• Parse complex schemas and metadata at scale.

• Use lineage to map dependencies and perform impact analysis.

• Propose structured migrations instead of direct writes.

• Validate in sandboxed environments before rollout.

• Follow safe deployment patterns with rollback options.

• Monitor drift and performance after changes go live.

This is the level of discipline required for automation in a write-heavy field like data engineering. Anything less risks breaking the very systems it is meant to improve.