Transformation Engineer

Global Data Systems Inc•Lafayette, LA

About The Position

Global Data Systems is transitioning from a Managed Services Provider to a Managed Intelligence Provider, beginning with its Customer Zero program. The program transforms twenty years of operational data—including tickets, SLA clocks, devices, and contracts—into managed intelligence used to run the company and later packaged for customers. The Transformation Engineer is a hands-on builder who will develop and operationalize Microsoft Fabric, AI, Copilot, and Power Automate solutions. The role focuses on moving promising builds into everyday use at GDS, then helping package and deploy proven capabilities for customers. It is a builder-first opportunity with room to grow into an architect role. The engineer will work with internal teams and customers to build data products, automation, and AI capabilities; support deployments and go-to-market activities; and contribute to governance and the AI Center of Excellence. The role also includes documenting solutions, demonstrating completed work, building reusable deployment assets, and helping ensure tools are supportable and appropriately governed.

Requirements

At least three years of hands-on experience building business solutions, automation, or data products, ideally using Microsoft tools.
Working experience with Power BI, including data modeling, DAX, or report building.
A track record of building and shipping solutions that people use, such as Power Automate flows, Power Apps, Copilot Studio agents, scripts, or integrations.
Curiosity about generative AI, a habit of trying new tools, and good judgment about data security and cost.
Ability to work with business users and customers to understand problems and turn them into working solutions.
Clear communication skills, including the ability to demonstrate work and write simple documentation others can follow.

Nice To Haves

Microsoft certifications such as Power BI Data Analyst (PL-300), Power Platform (PL-200 or PL-400), or Fabric Analytics Engineer (DP-600), or willingness to earn them in the first year.
Experience with ConnectWise or other PSA and service desk data.
Experience in a managed services, IT services, or consulting environment.
Microsoft Fabric experience.

Responsibilities

Build and maintain semantic models, reports, and data pipelines in Microsoft Fabric, and help consolidate operational sources into a trusted Lakehouse.
Help move AI capabilities from pilot to daily use, including agents, Copilot skills, and pipelines for commitment intelligence, contract extraction, and executive pre-reads.
Build and support Power Automate flows, Power Apps, and API connections to reduce manual work in service delivery, reporting, and account management.
Instrument important metrics so they are honest and complete, and ensure the same data reaches GDS teams and customers.
Work with the Service Desk, NOC, PMO, Finance, and account teams to understand problems and embed solutions in their workflows.
Document proven internal capabilities, their requirements, and their deployment processes so they can become repeatable customer offerings.
Take a hands-on role in early customer deployments, covering data readiness, build, rollout, and adoption.
Create reusable templates, model patterns, flow and agent libraries, and checklists to make deployments faster.
Bring lessons from customer work back into the Customer Zero platform to improve internal and customer tools.
Contribute to the Managed Intelligence offering with the VP of Customer Operations, Sales, and the Microsoft practice, providing a builder's perspective on delivery feasibility.
Demonstrate built solutions in customer briefings, workshops, and QBRs.
Help deliver Microsoft-funded engagements, including Copilot and agent workshops, internally and for customers.
Earn relevant Microsoft certifications and contribute to customer references supporting the Microsoft Copilot Specialization.
Maintain a current inventory of AI and automation tools, including ownership, data used, and usage status.
Follow and help shape governance practices for data sensitivity, security, and consumption cost.
Build solutions that are easy to support and hand off, and can adapt as platforms change.
Participate in the AI Center of Excellence, including office hours and “I Built This” sessions.