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Data & AnalyticsClient Project
Retail Analytics Platform
One warehouse and role-based BI dashboards over 200+ store systems
The client's name is withheld. Ask us to walk you through this build live.
Client
A multi-store retail group
Timeline
4-month build
PythonPostgreSQLApache AirflowKafkaReactD3.js
About This Project
A retail group operating more than 200 stores across three countries was reporting on week-old spreadsheets. This platform replaced that with a single governed picture of the business. Airflow pipelines pull from every store's POS and inventory systems into a central warehouse, and demand-forecasting models generate reorder recommendations. React dashboards give store managers, regional directors, and executives views scoped to their role, all reading from the same numbers.
The Engineering Problem
The hard part of multi-store analytics isn't the charts. It's making 200+ independently configured source systems tell one consistent story, fresh enough to act on.
- •Incompatible POS and inventory systems across stores, with divergent schemas and data quality
- •Reporting latency measured in days made the numbers historical rather than operational
- •Demand forecasting requires clean, aligned sales history the source systems didn't provide
- •Each management layer needs a different slice without forking the metric definitions
- •Pipelines must detect and quarantine bad data instead of silently polluting the warehouse
Technical Approach
We treated schema unification as the core deliverable. Every source maps into one dimensional model, every pipeline carries data-quality gates, and every dashboard reads from the same governed metric definitions.
- Designed a unified dimensional model absorbing every store system's schema
- Built Airflow DAGs with validation gates that quarantine anomalous loads for review
- Streamed high-volume sales events through Kafka into the warehouse alongside batch loads
- Trained demand-forecasting models producing reorder recommendations per store and SKU
- Defined metrics once in a semantic layer consumed by every role-scoped dashboard
How the Build Ran
Data Discovery & Modeling
2 weeksAudited the source systems across stores, mapped their schemas, and designed the unified warehouse model.
Pipeline & Warehouse Build
6 weeksBuilt the Airflow ETL pipelines and the PostgreSQL warehouse with data-quality gates and optimized query patterns.
Analytics & Dashboards
6 weeksDeveloped the forecasting models, D3.js visualizations, and the role-based React dashboard application.
Rollout & Enablement
2 weeksDeployed region by region, trained management teams, and stood up ongoing data-quality monitoring.
Key Features in This Build
Live Sales Views
Store-level sales flowing into the warehouse with drill-down by region, category, and product
Demand Forecasting
Per-store, per-SKU models feeding reorder recommendations
Role-Scoped Dashboards
Store, regional, and executive views reading one set of governed metrics
Data-Quality Gates
Validation at every pipeline stage with quarantine for anomalous loads
Build Scope
We designed and delivered all of the scope below on this engagement, and we can walk you through the pipelines, warehouse, and dashboards live.
200+
Source systems
Store POS and inventory feeds unified
3
Dashboard roles
Store, regional, and executive views
Airflow
Pipeline
Scheduled DAGs with data-quality gates
Kafka
Streaming
High-volume sales events alongside batch loads
Technology Stack
data-pipeline
- Apache Airflow
- Python
- Pandas
- Apache Kafka
database
- PostgreSQL
- Redis
- TimescaleDB
- AWS Redshift
frontend
- React
- TypeScript
- D3.js
- Tailwind CSS
visualization
- Recharts
- Plotly
- Custom D3 Components
- Tableau
Designed to Extend
Extensions this architecture is built to support:
- Computer-vision foot-traffic analysis feeding the same warehouse
- Price-optimization models on the existing feature store
- A mobile companion app for store managers
- Supplier scorecards extending the analytics schema
Want to See It Running?
Ask us to walk you through this build live, and to map the approach onto your product.
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