Zengines

Accelerate data migrations and modernizations by 80% using AI-powered tools that allow business users to trace, map, and move data without any coding expertise.

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About Zengines

Zengines is an AI-driven data transformation platform designed to simplify complex migrations and provide clarity into legacy mainframe systems. The tool addresses the "black box" problem often found in large-scale financial services, where decades of COBOL code and undocumented processes make moving data difficult. By automating the discovery and conversion process, Zengines enables organizations to transition from outdated infrastructure to modern cloud-based systems with lower manual effort. The software functions as a bridge between rigid legacy environments and modern architectures, ensuring institutional knowledge is preserved throughout the migration. The platform operates through two primary products: Mainframe Data Lineage and End-to-End Data Migration. The lineage tool acts as a diagnostic engine, translating legacy code into plain English and visual maps to reveal hidden calculation logic and data relationships. This allows users to understand the current state of their data before any movement occurs. Once the environment is mapped, the AI-powered migration module manages the entire lifecycle, including analysis, mapping, execution, and testing. This approach handles data movement with minimal technical intervention, turning a coding-heavy project into a business-led process. Targeted primarily at the financial services sector, Zengines is utilized by business analysts, digital transformation leads, and IT managers. It is effective for high-stakes scenarios such as M&A integrations, vendor changes, and complex customer onboarding where data accuracy is essential. Because it removes the requirement for specialized engineering resources to write custom migration scripts, it empowers business users who understand the data's context to lead the project directly. This shift in responsibility helps organizations avoid the technical bottlenecks that typically stall large-scale modernization efforts. What distinguishes Zengines from other ETL tools is its focus on the contextual intelligence of the source data. While many tools focus on the transfer of information, Zengines provides the intelligence needed to understand legacy systems first. Its ability to interpret COBOL and other mainframe languages using AI separates it from generic utilities. By reducing the reliance on specialized developers by approximately 80%, the platform transforms data migration from a high-risk technical hurdle into a predictable phase of digital transformation.

Zengines pros & cons

Pros

Reduces engineering resource requirements by 80% through AI-driven automation.
Translates complex mainframe COBOL code into plain English business context.
Accelerates data conversion projects by up to 80% compared to traditional methods.
Increases business analyst productivity by an average of six times.
Provides deep visibility into "black box" legacy systems to identify hidden data relationships.

Cons

Primarily focused on the financial services sector rather than general-purpose use.
Does not provide public pricing or self-service sign-up options on the website.
Website provides limited information regarding specific third-party technical integrations.

Zengines use cases

Business analysts in financial services can lead data conversion projects without waiting for engineering teams to write custom scripts.
IT modernization leads can use the data lineage tool to understand undocumented COBOL logic before planning a migration to the cloud.
M&A integration teams can consolidate data from acquired companies faster by using AI-powered mapping to bridge different application schemas.
Operations managers can streamline complex customer onboarding by automating the movement of client data into internal systems.

Zengines features

ai-powered data mapping
no-code business user interface
diagnostic research and discovery
data path visualization
legacy code translation (cobol)
automated data testing
end-to-end conversion lifecycle
mainframe data lineage

Zengines FAQs

Does Zengines require coding knowledge to perform data migrations?

No, the platform is specifically designed to allow business users to trace, map, and move data without any coding expertise. It automates technical tasks to reduce the need for engineering resources by up to 80%.

How does the Mainframe Data Lineage tool assist with migrations?

It functions as a diagnostic tool that analyzes "black box" legacy systems, such as those running on COBOL. It translates complex code into plain English and visual maps, revealing hidden data paths and calculation logic before the move begins.

Which industries is Zengines specifically built for?

While it can be used for various data projects, the platform specializes in the financial services sector. It is commonly used for M&A integrations, customer onboarding, and digital transformations within asset and wealth management.

Can Zengines help with M&A integrations?

Yes, the platform is designed to handle M&A integrations and system consolidations. It helps teams quickly map and move data between disparate applications to ensure a smooth transition during corporate changes.

What level of productivity increase can business analysts expect?

On average, Zengines reports a 6x increase in business analyst productivity. This is achieved by automating the discovery, mapping, and testing phases of the data conversion lifecycle.

Open roles

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Zengines

DevOps Engineer

engineering onsite full-time

Benefits:

Build infrastructure from the ground up - not maintain decades-old systems
Work on problems that matter - real enterprise deployments with real consequences
Learn by doing - tackle challenges that require creative problem-solving, not following runbooks
Direct impact on product and customers - see your work in production in days, not months
Equity stake in what we're building together

Experience Requirements:

5+ years of experience as a DevOps engineer or in similar infrastructure roles
Hands-on experience building and maintaining production cloud infrastructure
Experience supporting applications in production environments

Other Requirements:

Strong hands-on experience with AWS cloud services and architecture patterns
Proficiency with Docker and solid experience with Kubernetes for container orchestration
Experience implementing observability and error tracking solutions (such as Datadog, New Relic, or similar)
Solid scripting and automation skills using Python or Bash
Hands-on experience with Infrastructure-as-Code using Terraform
Solid understanding of networking, security groups, load balancers, and DNS
Experience building and maintaining CI/CD pipelines using tools like GitHub Actions, GitLab CI, or similar
Proficiency with Git and Linux system administration
Database operations experience supporting production systems
Strong problem-solving skills and ability to work effectively in fast-paced environments
Excellent verbal and written communication skills
Comfort with changing priorities and ability to adapt quickly

Responsibilities:

Build and maintain cloud infrastructure on AWS
Implement and optimize CI/CD pipelines to support smooth software delivery
Deploy and manage containerized applications using Docker and Kubernetes
Implement Infrastructure-as-Code using Terraform for provisioning and configuration management
Monitor system performance and application health using modern observability and error tracking platforms

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