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Data & Analytics · Retail & Commerce

E-Commerce Analytics Platform

For an online retailer, an analytics platform that brings order, product and customer data into one governed view, so trading meetings start from agreed numbers.

Built on
Python, Apache Airflow, PostgreSQL, Tableau
Industry
Retail & Commerce
Project type
Data & Analytics
Primary service
Data & Analytics
Scope
Data architecture, pipeline engineering, semantic modeling, dashboard design.

Repository not shown as per company policy

01Challenge

Every meeting started with an argument about the numbers.

Storefront, payment provider, warehouse system and a shared spreadsheet each held part of the picture, and each was treated as the source of truth by someone.

  • Four sources, four answers

    Refunds, partial shipments and canceled orders were handled differently in each system, so revenue and units never matched.

  • Manual reconciliation every week

    An analyst spent part of every week lining the systems up by hand before any reporting could start.

  • Silent data problems

    When a feed broke, nobody knew until a number looked wrong in a meeting.

02Approach

Model the business, then build the pipelines around it.

  • Agree the vocabulary

    Trading, marketing and finance signed off one definition per metric, recorded alongside the model that computes it.

  • Design around definitions, not systems

    Pipelines were shaped by what the business needed to measure, rather than by whichever system happened to hold the data.

  • Keep history

    Daily snapshots make period-on-period comparison reliable even when source systems overwrite records.

03Outcome

One set of numbers, agreed before anyone builds a dashboard.

The retailer did not lack reports. It had too many, each reading a different system and each giving a slightly different answer to the same question.

  • Definitions first

    What counts as an order, a returning customer or a sold unit was written down and agreed before a single pipeline was built.

  • One semantic layer

    Every dashboard reads its metrics from the same governed definitions, so two reports cannot disagree about revenue.

  • Failures are loud

    A pipeline that fails or loads suspicious data raises an alert before the morning trading meeting, not during it.

What changed

  • Trading and marketing teams work from one agreed set of metric definitions.
  • Weekly reporting that used to be reconciled by hand across systems is produced from the warehouse.
  • Data quality failures raise an alert before they reach a dashboard.

How it works

From four source systems to one trading view.

StorefrontOrders, carts, customersPaymentsCaptures, refundsFulfillmentShipments, returns, stockGoverned modelPostgreSQL, daily snapshotsSemantic layerOne definition per metricTrading dashboardDaily decisionsPeriod comparisonWeek, month, seasonPipelinesOrchestrated with Apache AirflowScheduled ingestionEvery source, on a scheduleQuality checksCompleteness, freshness, reconciliation

One run, step by step.

01 / 05

Inside the system

What it is made of, and what keeps it safe.

What it does.

  • Scheduled ingestion from storefront, payments and fulfillment systems
  • Governed metric definitions shared across all reporting
  • Data quality checks with alerting on pipeline failure
  • Operational dashboard for daily trading decisions
  • Historical snapshots for period-on-period comparison

Built with

What it runs on.

Pipelines
PythonApache Airflow
Warehouse
PostgreSQLDaily snapshotsSemantic layer
Reporting
Tableau
Operations
Data quality checksFailure alerting
Services appliedData & AnalyticsTechnology Solutions

Trading meetings now start from the same numbers.

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