Data Analyst

Position Details Job Title: Data Analyst — Operations & Supply Chain Department: Operations Location: Dubai, United Arab Emirates (on-site) Reports To: Chief Operating Officer / Head of Operations Direct Reports: None (individual contributor) Experience Required: 5–7 years Employment Type: Full-time, permanent About the Business We are a fast-growing international fashion group operating a portfolio of luxury, premium and streetwear brands across EMEA and APAC, with the regional headquarters in Dubai and additional offices in Europe. The business runs a hybrid model — B2B wholesale to department stores, distributors and key accounts (approx. 85% of volume) alongside a direct B2C eCommerce and marketplace channel. Scale is in the region of 5–6 million units per year, 20,000+ active SKUs, sourced primarily from Bangladesh, Vietnam, Turkey and China, and distributed through a network of outsourced 3PL distribution centres in Germany, the UK, the Middle East and Asia. The organisation is in an active build-out and transformation phase: new ERP landscape (Microsoft Dynamics 365 Business Central), new warehouse partners, new systems and new processes. This is a role for someone who wants to build the function, not inherit a finished one. Job Purpose The Data Analyst is the analytical engine of the operation. The role exists to turn raw system data — ERP, warehouse, eCommerce, carrier and supplier — into reporting, KPIs, cost visibility and decisions. This is not a passive reporting role. The analyst is expected to define the question, build the model, find the money, and follow the recommendation through to a measurable result. The role also owns the operational data governance behind new customer and new SKU onboarding, ensuring that master data is correct before it reaches the warehouse and the customer. Daily, fluent use of AI tools is a core requirement of this role — not an optional extra. We expect the successful candidate to work materially faster than a traditional analyst because of how they use these tools. Key Responsibilities 1. Reporting & Business Intelligence • Build, automate and maintain the operational reporting suite — daily operational reports, weekly performance packs and monthly management reporting. • Replace manual, spreadsheet-based reporting with automated, self-serve dashboards. • Define a single source of truth for operational data and reconcile conflicting reports across systems and teams. • Deliver clear, decision-ready outputs for Operations, Commercial, Finance and Executive management. 2. KPI Framework & Performance Tracking • Define, baseline and track the operational KPI framework across the supply chain: OTIF, order cycle time, dock-to-stock, pick and pack accuracy, dispatch performance, carrier delivery performance, returns rate, inventory accuracy, cost per unit / per order / per shipment. • Produce monthly performance reviews with variance analysis, root cause commentary and corrective actions — not just numbers. • Run performance reporting against 3PL and carrier SLAs and support contractual performance discussions with hard data. • Track improvement initiatives end to end and quantify realised versus promised benefit. 3. Cost Analysis & Savings Identification • Build and maintain full visibility of the logistics and operational cost base: 3PL warehousing and handling, freight (sea, air, road), customs and duty, parcel and B2B carriers, returns processing and packaging. • Validate supplier and 3PL invoices against contracted rate cards; identify overbilling, duplicate charges, unagreed surcharges and billing leakage, and drive recovery. • Build cost-per-unit and cost-to-serve models by lane, channel, distribution centre, brand and customer. • Identify, quantify and present savings opportunities with a clear business case; track savings from identification through to realisation in the P&L. • Support tenders, RFQs and contract negotiations with volume analysis, scenario modelling and provider comparison. • Support annual operational budgeting and monthly cost forecasting with bottom-up models. 4. ERP & Data Infrastructure • Extract, model and reconcile data from the ERP (Microsoft Dynamics 365 Business Central) and all connected systems — WMS, eCommerce platform, marketplaces, carrier portals, customs and freight forwarder systems. • Understand the underlying data structures and table relationships well enough to build reliable reporting on top of them. • Identify data quality issues, trace them to root cause in process or system, and drive permanent fixes rather than manual workarounds. • Work closely with IT on data extracts, integrations, report development and system changes affecting operational data. 5. New Customer & New SKU Onboarding • Own the operational data setup for new B2B customers: account master data, delivery and routing requirements, labelling and packaging specifications, EDI and portal requirements, SLA parameters and special handling rules. • Own governance of new SKU and item creation — completeness and accuracy of attributes, barcodes, dimensions and weights, HS codes, country of origin, cost fields and channel readiness. • Build and maintain the checklists and controls that ensure master data is right first time, before it reaches the warehouse or the customer. • Report on onboarding status, lead times and data completeness, and chase the gaps to closure. 6. AI, Automation & Tooling (mandatory) • Use AI and large language model tools daily to accelerate analysis, generate and debug code, automate reporting, and extract structured data from unstructured documents (invoices, packing lists, customs documents, contracts). • Build small internal tools and scripts that remove recurring manual work from the operation. • Identify, prototype and propose AI and automation use cases across the operational landscape, and quantify the time and cost saved. • Stay current with available tooling and raise the technical capability of the wider operations team. Candidates will be asked, in the interview, to describe and demonstrate concrete examples of how they use AI tools in their current role. 7. Ad-Hoc Analysis & Decision Support • Answer executive and cross-functional business questions with fast, accurate, well-structured analysis. • Build scenario and what-if models: demand and supply availability, warehouse capacity, network design, peak season planning, carrier mix. • Prepare analysis and supporting material for management, board and partner reviews. Qualifications & Experience • 5–7 years of experience as a data analyst, business analyst, supply chain analyst or operations analyst, ideally in retail, eCommerce, fashion, FMCG or logistics. • AI tools — mandatory: demonstrable, daily, hands-on use of AI / LLM tools in a professional context, with concrete examples of work delivered faster or differently because of them. • Excel: expert level — complex modelling, large data sets, reconciliation and cost models.

5–7 years of experience as a data analyst, business analyst, supply chain analyst or operations analyst, ideally in retail, eCommerce, fashion, FMCG or logistics. AI tools — mandatory: demonstrable, daily, hands-on use of AI / LLM tools in a professional context, with concrete examples of work delivered faster or differently because of them. Excel: expert level — complex modelling, large data sets, reconciliation and cost models.

1. Reporting & Business Intelligence: Build, automate and maintain the operational reporting suite — daily operational reports, weekly performance packs and monthly management reporting. Replace manual, spreadsheet-based reporting with automated, self-serve dashboards. Define a single source of truth for operational data and reconcile conflicting reports across systems and teams. Deliver clear, decision-ready outputs for Operations, Commercial, Finance and Executive management. 2. KPI Framework & Performance Tracking: Define, baseline and track the operational KPI framework across the supply chain: OTIF, order cycle time, dock-to-stock, pick and pack accuracy, dispatch performance, carrier delivery performance, returns rate, inventory accuracy, cost per unit / per order / per shipment. Produce monthly performance reviews with variance analysis, root cause commentary and corrective actions — not just numbers. Run performance reporting against 3PL and carrier SLAs and support contractual performance discussions with hard data. Track improvement initiatives end to end and quantify realised versus promised benefit. 3. Cost Analysis & Savings Identification: Build and maintain full visibility of the logistics and operational cost base: 3PL warehousing and handling, freight (sea, air, road), customs and duty, parcel and B2B carriers, returns processing and packaging. Validate supplier and 3PL invoices against contracted rate cards; identify overbilling, duplicate charges, unagreed surcharges and billing leakage, and drive recovery. Build cost-per-unit and cost-to-serve models by lane, channel, distribution centre, brand and customer. Identify, quantify and present savings opportunities with a clear business case; track savings from identification through to realisation in the P&L. Support tenders, RFQs and contract negotiations with volume analysis, scenario modelling and provider comparison. Support annual operational budgeting and monthly cost forecasting with bottom-up models. 4. ERP & Data Infrastructure: Extract, model and reconcile data from the ERP (Microsoft Dynamics 365 Business Central) and all connected systems — WMS, eCommerce platform, marketplaces, carrier portals, customs and freight forwarder systems. Understand the underlying data structures and table relationships well enough to build reliable reporting on top of them. Identify data quality issues, trace them to root cause in process or system, and drive permanent fixes rather than manual workarounds. Work closely with IT on data extracts, integrations, report development and system changes affecting operational data. 5. New Customer & New SKU Onboarding: Own the operational data setup for new B2B customers: account master data, delivery and routing requirements, labelling and packaging specifications, EDI and portal requirements, SLA parameters and special handling rules. Own governance of new SKU and item creation — completeness and accuracy of attributes, barcodes, dimensions and weights, HS codes, country of origin, cost fields and channel readiness. Build and maintain the checklists and controls that ensure master data is right first time, before it reaches the warehouse or the customer. Report on onboarding status, lead times and data completeness, and chase the gaps to closure. 6. AI, Automation & Tooling (mandatory): Use AI and large language model tools daily to accelerate analysis, generate and debug code, automate reporting, and extract structured data from unstructured documents (invoices, packing lists, customs documents, contracts). Build small internal tools and scripts that remove recurring manual work from the operation. Identify, prototype and propose AI and automation use cases across the operational landscape, and quantify the time and cost saved. Stay current with available tooling and raise the technical capability of the wider operations team. Candidates will be asked, in the interview, to describe and demonstrate concrete examples of how they use AI tools in their current role. 7. Ad-Hoc Analysis & Decision Support: Answer executive and cross-functional business questions with fast, accurate, well-structured analysis. Build scenario and what-if models: demand and supply availability, warehouse capacity, network design, peak season planning, carrier mix. Prepare analysis and supporting material for management, board and partner reviews.