How We Reduced a Multi-Hour SFMC Automation to 30 Minutes Using a Dynamic SSJS Engine

Nov 22, 2025 · 10 min read

How We Reduced a Multi-Hour SFMC Automation to 30 Minutes Using a Dynamic SSJS Engine

A deep-dive into how dynamic scripting, automated Data Extension generation, and query orchestration can transform large-scale SFMC systems.

By Eshwar Vemula

Salesforce Marketing Cloud Optimization Specialist

⸻

Overview

As Salesforce Marketing Cloud implementations expand, segmentation logic, data flows, and automations often grow faster than teams can manage them. Over time, many organizations find themselves operating massive, slow-moving automation frameworks filled with thousands of SQL Query Activities and Data Extensions.

Recently, our team redesigned a legacy SFMC workflow that:

We replaced this architecture with a fully dynamic, SSJS-driven automation engine.

The outcome?

This article explains the architecture, design approach, and business impact.

⸻

The Challenge: The “One Query Per Segment” Architecture

Many SFMC environments follow a pattern that eventually becomes unmanageable:

Each segment stored its output in a separate, dynamically-named DE.

Every DE required its own QueryDefinition.

Hundreds of query activities executed sequentially, increasing runtime and risk of failure.

A small business logic update meant:

This design works temporarily, but not at enterprise scale.

⸻

The Solution: A Dynamic SSJS Processing Engine

To eliminate manual maintenance and improve performance, we built a centralized Dynamic Per-Segment SQL Engine using SSJS and WSProxy.

The engine performs all work dynamically:

One script replaced the entire legacy structure.

⸻

Key Capabilities of the New Engine

#1. High-Performance Batch Processing

Segments run in controlled batches (e.g., 400 per cycle), which:

⸻

#2. Dynamic SQL Generation

A single SQL template is used for every segment.

Variables such as:

are inserted dynamically.

This removes the need to maintain thousands of SQL files.

⸻

#3. Automatic Data Extension Creation

If an output DE doesn’t exist, the script generates it with:

This enables rapid onboarding of new segments.

⸻

#4. Self-Healing QueryDefinitions

The script intelligently handles QueryDefinitions:

This ensures consistent, error-free execution.

⸻

#5. Execution via WSProxy

Using WSProxy to start QueryDefinitions ensures:

⸻

#6. Centralized Logging Framework

Every segment execution logs:

This provides audit-friendly observability and simplified troubleshooting.

⸻

#7. Safe Restart Logic

Segments are marked as processed only after the query succeeds.

This ensures:

⸻

Before vs. After

Legacy System

Optimized System

⸻

Business Impact

Metric

Before

After

Runtime

4+ hours

~30 minutes

SQL Activities

1000+

0

Automation Steps

Hundreds

2

Logic Change Time

Hours/days

Minutes

Scalability

Low

Extremely High

Reliability

Fragile

Fully Logged & Stable

The organization gained a future-proof framework that reduces operational costs and improves data readiness for marketing activations.

⸻

Conclusion

This project demonstrates how legacy SFMC architectures can be transformed using modern, dynamic scripting models. A single SSJS engine can replace thousands of static components, enabling:

By modernizing the architecture, the organization now processes segments in minutes rather than hours—and updates logic in seconds rather than days.