Luxury Retailer Reshapes Engineering Workforce with Draup's Work Redesign Agent

A Middle East luxury retail conglomerate used Etter to unify six engineering sub‑teams into one taxonomy, model three AI adoption scenarios against real headcount, and put a CTO‑grade transformation case in front of its steering committee in under four weeks.

~14,000

Employees

$3B+

Revenue

Luxury Retail

Industry

Dubai, UAE

Headquarters

About the organization

One of the Middle East’s leading luxury retail conglomerates, operating a multi‑brand portfolio across the GCC anchored by flagship department stores and extended through fashion, beauty,and lifestyle properties. Its technology and digital organization spans Engineering, Data and Analytics, and Omnichannel, staffed through a blended full‑time and contractor model across onshore and offshore teams. With omnichannel retail moving fast and AI reshaping how engineering work gets done, leadership wanted an evidence‑based view of how the function would change, what capability it would need, and what the shift would cost.

The Core Challenges

Unifying a Multi-Team Engineering Org
Six engineering sub‑teams, each running its own tech stack  and skill profile. Leadership had no single analytical view to anchor enterprise‑wide workforce decisions.
Harmonizing a Contractor-Heavy Workforce
Contractors and FTEs sat in separate structures with  many‑to‑many role and skill relationships. AI impact analysis could not scale  until the taxonomy was resolved.
Sharpening AI Framing for Leadership Action
Leadership wanted to move beyond generic productivity claims and build a team-specific, name-level workforce transformation narrative one detailed enough for the CTO and steering committee to act directly.
Defining Adoption Scenarios with Clarity
Cautious, balanced, and aggressive had to mean something operationally: defined headcount shapes, explicit tool additions, and clear  sunset decisions.

The Solution

The organization used Draup to deploy its Etter workforce intelligence platform across the engineering function, beginning with two pilot teams, Data Engineering (17 people: 11 FTEs and 6 contractors) and Platform Engineering. The engagement was structured as a bottom-up rollout, aggregating team-level analysis into a function-wide view.

01
Role & Taxonomy Harmonization
The organization's internal org structure is being harmonized with capability classifications from its external advisory inputs, resolving a many-to-many mapping between roles, skills, and capabilities across contractor and FTE pools establishing a clean, unified taxonomy as the foundation for all downstream analysis.
02
Team-Level Pyramid Modeling
For each sub-team, current-state headcount pyramids are produced by designation level alongside target-state pyramids under balanced and aggressive adoption scenarios, with decimal level headcount representation to reflect partial role absorption accurately.
03
Skill Gap & Tech Stack Analysis
Each role is mapped against current skills and tech stack, with future skill requirements identified per scenario clearly differentiating between sunset workloads, tools being added, and tools being phased out, giving leadership a precise transition view per team.
04
Productivity & ROI Scenario Modeling
Each role is mapped against current skills and tech stack, with future skill requirements identified per scenario clearly differentiating between sunset workloads, tools being added, and tools being phased out, giving leadership a precise transition view per team.

Outcome

Team-Level Transformation View Delivered

Data Engineering analysis is surfacing one associate-level role reduction under a balanced scenario and full absorption of Power BI developer workload by senior data engineers under the aggressive scenario giving leadership precise, actionable headcount implications at the team level.

Function-Level Business Case Established

Platform Engineering, a three-person team with limited standalone ROI, is being folded into an org-level business case showing that cross-function tool deployment across engineering generates viable returns shifting the conversation from team ROI to function ROI.

Steering Committee-Ready in Under Four Weeks

From data ingestion to steering committee-ready outputs, each engagement cycle moves in approximately three to four weeks, helping the organization translate workforce data into a CTO-ready AI transformation narrative within a single month.

Scalable Framework for Full Org Rollout

The analysis framework is built to extend across all remaining engineering sub-teams and into Omnichannel and Corporate functions, with future-state job descriptions and reskilling pathways mapped to the organization's existing Coursera and EDX course providers surfacing as downstream outputs.
6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
Labor Market Data Contextualized with Business Intelligence for Every Decision

Analyzing 1B+ data points
daily from 100K+ sources

6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
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