Assistant Manager, Data & BI

About Unilever
At Unilever you are more than your job title, you are part of the world’s most successful, purpose-led business. Work with brands that are well-loved around the world, that improve the lives of our consumers and the communities around us.
We produce world-leading brands including Lipton, Knorr, Dove, Axe, Hellmann’s and Omo, alongside trusted local names and innovative-forward thinking brands like Ben & Jerry’s, The Dollar Shave Club and Surf.
Be part of the most successful purpose-led business in the world. Have the opportunity to see the true impact that you’re having in the work you do - every small thing counts.

Job Purpose

We are looking for an Assistant Manager – Data & BI to own and operate the commercial data, business intelligence and analytics ecosystem for Unilever Arabia's Customer Business Development function. The role covers 6 GCC markets, 45+ retail customers and all business groups.
You will be responsible for the full data lifecycle — data ingestion, ETL, data modelling and master data — and for turning that data into reliable dashboards, automated reporting and commercial insights. You will analyse growth, market share, pricing and customer performance, run deep dives, and support Joint Business Planning and business reviews.
This is a hands-on role with team leadership responsibility. You will partner with CBDs and CSPs, and lead a small team of analysts.
What Will be Your Responsibilities
• Manage and maintain a high-volume data estate of 1,700+ daily, weekly and monthly files
• Cover 15+ data domains, including sell-in, sell-out (EPOS), distribution, trade stock, execution and syndicated market data
• Support 6 GCC markets, 45+ retail customers and all business groups
• Run BI platforms used by 300+ business users and live trackers used by 100+ field sales users
• Lead a team of up to 6 analysts
Role scope and complexity
This role offers broad, end-to-end ownership across the commercial data landscape:
• Breadth of data – many sources, formats and refresh cycles across retailers, distributors, internal systems and syndicated providers, all integrated into one regional view.
• Depth of technical work – from data pipelines and data models through Power BI administration, automation and machine learning.
• Regional impact – the data and dashboards managed in this role support commercial planning and decisions across all markets and business groups.
• Continuous development – new data sources, markets, KPIs and business needs are added regularly, so the role combines steady operations with ongoing build work.
Key responsibilities
1. Data management & data engineering
• Manage and maintain 1,700+ daily, weekly and monthly files from retailers, distributors, SAP and syndicated providers, arriving in different structures, granularities, currencies and units.
• Own core commercial datasets end to end, including sell-in, sell-out/EPOS, distribution, trade stock and execution (OSA, Share of Shelf).
• Build and maintain ETL pipelines (Power Query, Python, SQL, Azure Data Factory, Databricks) that handle schema changes, duplicates, missing values, invalid barcodes, unit conversions and late or partial submissions.
• Run data-quality checks every cycle: completeness, variance against prior periods, outlier detection and cross-source reconciliation.
• Reconcile sell-in, sell-out and trade stock by market, customer, category and SKU.
• Maintain product, customer and hierarchy master data across all markets and business groups.
• Design and maintain data models that serve as one governed source of truth for all reporting.
2. Sell-out (EPOS) data & product mapping
• Manage the monthly sell-out cycle for all retail customers: collection, cleansing, mapping, validation and publishing.
• Map retailer SKUs to the Unilever and competitor product hierarchy to keep market share and competitor analysis complete.
• Maintain and improve the machine-learning mapping pipeline (Python) using exact, fuzzy and semantic matching, nearest-neighbour search, historical consensus and rule-based hierarchy checks.
• Monitor model accuracy on validation data, manage confidence thresholds and run the manual-review process for uncertain items.
3. Power BI development & administration
• Develop and maintain the region's Power BI platforms and dashboards, including sell-in, sell-out, execution (OSA, Share of Shelf, distribution), trade stock and commercial scorecards.
• Build data models and advanced DAX measures, including time intelligence, dynamic period switching (MAT, YTD, L12, TM), unit toggles, ranking, growth and market share.
• Manage row-level security (RLS), user access and workspace and app permissions.
• Manage scheduled refresh, incremental refresh and gateway connections, and monitor and resolve refresh failures.
• Handle report versioning, publishing and deployment, and keep dashboards updated as new data, customers and KPIs are added.
• Tune model and report performance on large datasets.
4. Automation
• Identify manual and repetitive reporting tasks and automate them end to end.
• Build and maintain Power Automate flows for email-attachment capture, file routing to SharePoint/OneDrive, refresh triggers, failure alerts and scheduled report distribution.
• Build reusable Power Query, Python and advanced Excel solutions to reduce manual file handling.
• Standardise file templates and formats with data owners to improve data flow and reduce rework.
5. AI & machine learning
• Develop and maintain machine-learning models that improve data accuracy and efficiency, such as product classification and mapping.
• Build and support AI agents and conversational analytics tools that help business users access data and insights.
• Explore new AI and GenAI use cases for commercial analytics, working with IT on platform access, security and governance (Databricks, Azure, Power BI Copilot, AI Foundry).
6. Commercial analytics & insights
• Analyse growth, market share, pricing, distribution and customer performance across markets and business groups.
• Run deep dives into growth drivers, share movement, stock risks and competitor activity.
• Support Joint Business Planning, customer reviews and business reviews with clear analysis and presentations.
• Run pricing analysis, including price indices, price architecture and price-change impact.
• Build scenario models to support commercial decisions, such as trade spend allocation.
7. New opportunities & analytics development
• Identify new growth opportunities and risks in the data, such as distribution gaps, out-of-stocks, under-performing SKUs and share shifts.
• Take new business questions from scoping to analysis to recommendation.
• Design and launch new dashboards and analytical tools as new needs, markets, customers or data sources come in.
• Onboard new data sources and markets into the regional data ecosystem.
• Deliver ad-hoc analysis to tight timelines.
8. Team leadership & governance
• Lead, coach and develop a team of up to 6 analysts, and plan work across daily, weekly and monthly cycles.
• Prioritise requests and manage the analytics roadmap with stakeholders.
• Maintain data governance standards: KPI definitions, naming conventions, validation rules, documentation and runbooks.
• Train business users and promote self-serve analytics.

What You Need to Succeed:

• Bachelor's degree in Engineering, Computer Science, Statistics, Business or related field.
• 7+ years in business intelligence, commercial analytics or data roles, ideally in FMCG, retail or ecommerce.
• Strong command of Power BI (data modelling, DAX, Power Query) and SQL.
• Python for data processing, automation and matching/ML (pandas, RapidFuzz, scikit-learn or similar).
• Solid understanding of FMCG commercial metrics: sell-in, sell-out, market share, trade stocks, DOC/DRR, TTS and P&L.
• Proven track record of building reporting from scratch and automating manual processes.
• Ability to explain data simply to non-technical and senior audiences, and experience leading a small analytics team.

Nice to have

• Experience with Microsoft Fabric, Databricks or lakehouse platforms.
• Hands-on experience building AI agents or GenAI solutions (M365 Copilot / Agent Builder, Azure AI Foundry, LLM APIs).
• Experience with GCC retailer sell-out data and syndicated data (Nielsen, Dunnhumby).
• Experience with Azure Data Factory or similar ETL tools.
  

Unilever embraces diversity and encourages applicants from all walks of life! This means giving full and fair consideration to all applicants and continuing development of all employees regardless of age, disability, gender reassignment, race, religion or belief, sex, sexual orientation, marriage and civil partnership, and pregnancy and maternity.

Unilever is an organisation committed to equity, inclusion and diversity to drive our business results and create a better future, every day, for our diverse employees, global consumers, partners, and communities. We believe a diverse workforce allows us to match our growth ambitions and drive inclusion across the business. At Unilever we are interested in every individual bringing their ‘Whole Self’ to work and this includes you! Thus if you require any support or access requirements, we encourage you to advise us at the time of your application so that we can support you through your recruitment journey.