Empowering a High-Growth E-Commerce Platform with a Modern Data Stack
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Empowering a High-Growth E-Commerce Platform with a Modern Data Stack

A rapidly growing social commerce platform that makes essential products affordable and accessible for millions of Indian households. With its unique community-led buying model, the company has quickly become a trusted name in value-driven retail.

The Problem Statement

The e-commerce platform faced growing challenges with its Redshift-based data infrastructure. Rising costs, inefficient queries, and fragile orchestration limited scalability. These bottlenecks slowed analytics, created reporting inconsistencies, and prevented business and product teams from leveraging data for timely, impactful decisions. 

Challenges

High Query Costs

Inefficient queries drove escalating costs and unsustainable data processing overhead.

Limited Scalability

Infrastructure struggled to support rapidly growing data volumes and evolving business needs.

Manual Orchestration

AWS-native jobs created brittle pipelines lacking modularity, monitoring, and failure resilience.

Unreliable Reporting

Frequent dashboard failures and inconsistent data freshness hindered timely decision-making.

Weak Governance

Limited access controls and a lack of masking exposed sensitive business-critical information.

No Personalization Layer

Absence of intelligent recommendations restricted product discovery and repeat customer engagement.

Solutions

Seamless Migration

Transitioned from Redshift to BigQuery with complete SQL parity and zero disruption. 

Optimized Performance

Implemented partition pruning and optimized schema design, reducing average query scan size from 98 GB to 2 GB – a 98% reduction. 

Automated Orchestration

Implemented Mage pipelines with monitoring, retries, and Slack-based failure alerts. 

Robust Governance

Enforced IAM roles, data masking, and audit logging for compliance and security. 

BI Enablement

Built reliable Tableau and Looker dashboards with semantic layer and freshness alerts. 

Personalization Layer

Developed recommendation engine and Buy Again widget, driving engagement and repeat purchases. 

Outcomes

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Query scan size reduced by 98%, delivering faster analytics and significant cost efficiency gains.

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Daily compute costs lowered by nearly 20% through optimized slot commitments and predictable pricing model. 

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Robust monitoring and alerting eliminated dashboard failures, ensuring consistent, reliable reporting across business-critical functions.

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Batch workflows empowered analysts with faster insights, improving productivity without adding infrastructure or operational overhead. 

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Personalized recommendations and optimized Buy Again widget boosted user engagement, product discovery, and repeat purchases. 

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Fraud detection framework reduced promotional misuse and financial leakage, strengthening platform trust and operational integrity.

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