Dynamic Pricing Engine Adjusting 50,000 SKU s in Real-Time and Increasing Revenue by 8%
An e-commerce company priced products weekly based on competitor snapshots. We built a real-time pricing engine using Kafka and Databricks with competitor monitoring, demand signals, and inventory levels — increasing revenue by 8%.
About the Client
The Challenge
An e-commerce company priced products weekly based on competitor snapshots. We built a real-time pricing engine using Kafka and Databricks with competitor monitoring, demand signals, and inventory levels — increasing revenue by 8%. The organization had reached an inflection point — customer data was fragmented across POS systems, e-commerce platforms, loyalty programs, and marketing tools. The merchandising team made decisions based on last week's numbers. Store operations still relied on paper processes and phone calls to headquarters.
The retail industry added specific complexity. PCI-DSS for payment processing, consumer privacy regulations (CCPA/GDPR), and omnichannel data governance demanded auditable processes and governance. Any technology initiative needed to maintain compliance continuity while delivering measurable improvement. Previous attempts had stalled because vendors didn't understand these industry-specific constraints.
The executive sponsor set clear expectations: demonstrate measurable impact within one quarter. No 18-month roadmaps. No theoretical architectures. Working software, real data, measurable results — or the budget moves elsewhere. They needed a partner who could deliver data engineering solutions with retail domain expertise from day one.
Our Approach
We designed a phased approach optimized for speed-to-value while maintaining PCI-DSS for payment processing, consumer privacy regulations (CCPA/GDPR), and omnichannel data governance continuity:
Assessment & Architecture (Weeks 1-2)
Cataloged source systems, data volumes, quality issues, and PCI-DSS for payment processing, consumer privacy regulations (CCPA/GDPR), and omnichannel data governance compliance requirements. Designed target data platform architecture with medallion layers and governance framework.
Ingestion Pipelines (Weeks 2-5)
Built automated data pipelines for all source systems with error handling, retry logic, and lineage tracking. Parameterized templates for consistent pipeline quality.
Transformation & Quality (Weeks 3-7)
Implemented Bronze → Silver → Gold transformations. Data quality checks at each layer. Industry-specific business logic and domain models in Gold layer.
Analytics & Consumption (Weeks 5-9)
Connected Gold datasets to Power BI semantic models with row-level security. Built domain-specific dashboards and self-service datasets for business users.
Governance & Handoff (Weeks 7-10)
Deployed governance framework with data classification, automated lineage, and access policies. Trained internal data team on platform operations and extension.
Solution Architecture
Platform: Lakehouse architecture with medallion layers (Bronze → Silver → Gold) and governance framework
Ingestion: Automated pipelines with error handling, retry logic, and lineage tracking
Consumption: Power BI semantic models with row-level security and certified datasets
Results
Technologies Used
Key Takeaways
If your organization is facing a similar challenge, here's what we learned:
Industry context eliminates weeks of discovery. Understanding retail terminology, PCI-DSS for payment processing, consumer privacy regulations (CCPA/GDPR), and omnichannel data governance, and operational workflows meant we skipped the "teach us your business" phase. Our data engineering team brought domain context from the first workshop.
Phased delivery maintains executive sponsorship. By delivering measurable results in 8-12 weeks, the sponsor had proof for their next board meeting. This is critical in retail organizations where budget cycles are tight and competing priorities are constant.
User adoption is the real success metric. Technology implementations fail when users don't adopt. We designed the solution around existing retail workflows — not the other way around. The system met users where they already worked, driving 80%+ adoption within the first month.
Ongoing governance prevents value decay. We established review cadences, defined data ownership, and built monitoring dashboards that make issues visible early. The platform continues to deliver value because governance is sustained — not because the initial deployment was perfect.
Facing a Similar Challenge?
We deliver data engineering solutions for retail organizations — with measurable outcomes typically within 8-12 weeks.