Data & Business Analyst

Pangaia

We are seeking a technically skilled and detail-oriented Data & Business Analyst to be a cornerstone in building our data-driven decision-making capability.

PANGAIA is a D2C brand with most sales via its webshop, and we are now planning a significant expansion of our retail activities. Building a strong analytics backbone to support both online and offline businesses is pivotal to provide leadership with a robust data basis for strategic and operational decisions.

In the initial phase, the focus will be on data consistency across systems and departments, implementing a master data concept, and setting up a scalable analytics infrastructure and logic. Once this foundation is in place, the role will evolve into delivering advanced analytics, predictive models, and actionable insights across both e-commerce and retail operations.

Key Responsibilities

Data Infrastructure & Governance

• Support the rollout of a master data management concept across all systems (ERP, PLM, CRM, POS, e-commerce).
• Ensure data accuracy, consistency, and availability across D2C and retail channels.
• Design data pipelines that integrate transactional, operational, and customer data.
• Establish governance processes for data ownership, taxonomy, and hierarchy.

E-Commerce & Retail Analytics

• Build dashboards and reports that cover both online and retail performance.
• Provide KPIs and insights for e-commerce (traffic, conversion, AOV, CAC, LTV, returns) and retail (footfall, basket size, sales per sqm, conversion rates).
• Support omni-channel analytics such as customer journey tracking across online and retail touch points.
• Monitor product performance across SKUs, categories, and geographies.

Predictive & Advanced Analytics

• In coordination with the commercial team on developing demand forecasting models combining internal sales data with external data (seasonality, macroeconomic trends, weather, events).
• In coordination with commercial teams, build predictive analytics for inventory planning, replenishment, and allocation across e-commerce and retail.
• Use AI/ML to create customer segmentation models, churn predictions, and personalized recommendations.
• Continuously refine and validate models to enhance decision-making quality.

Operational & Business Insights

• Analyse the full value chain: sourcing, logistics, fulfillment, and returns.
• Provide insights into process efficiency, lead times, and cost optimisation.
• Enable leadership to make informed decisions on pricing, promotions, and assortment strategy.
• Support finance with margin and profitability analytics across sales channels.

Customer-Facing Insights

• Create analytics frameworks for customer service performance, NPS, and satisfaction tracking.
• Monitor omni-channel customer engagement (CRM campaigns, loyalty programs, retail events).
• Support marketing funnel optimization with data-backed insights.

Technical Skills

• Strong command of SQL and experience with data warehouses (Snowflake, Big Query, Azure).
• Programming skills in Python or R for analysis, modelling, and machine learning.
• Experience with ETL/ELT pipelines and data integration tools (dbt, Fivetran, Airflow, Celigo).
• Proficiency with visualisation tools (Power BI, Tableau, Looker).
• Familiarity with cloud environments (Azure, AWS, GCP).
• Understanding of APIs and integrations across ERP, CRM, PLM, POS, and e-commerce platforms.

Analytics & Modeling

• Expertise in forecasting, predictive modelling, and machine learning.
• Strong statistical background with ability to apply regression, clustering, time-series analysis, A/B testing.
• Deep knowledge of retail and e-commerce KPIs.
• Ability to create omni-channel analytics frameworks for unified reporting.

Capabilities for D2C + Retail Support

• Experience combining online and offline data sets for holistic customer and business insights.
• Familiarity with POS data structures and retail analytics metrics (traffic counters, sales density).
• Knowledge of inventory optimization across multiple distribution channels.
• Understanding of customer behavior analytics both online (clickstream, funnels) and in-store (footfall, dwell time).
• Awareness of sustainability metrics relevant for premium fashion (returns rates, circularity, DPP readiness).