How Etam’s Data Team Scales Monitoring and ROI with Sifflet

Etam is a French fashion retailer known for its lingerie, loungewear, and activewear collections. With over a century of history and a modern, data-driven operation, Etam relies on Snowflake and dbt to power reporting across five key business domains. But with a lean team managing a high volume of data, they needed a smarter way to catch errors before they reached the business.

Modern Stack, Blind Spots: Etam Lacked Visibility into Costly Data Issues

Etam's data team had recently completed its migration to Snowflake and implemented dbt to manage data transformations. But while their technical stack was robust, the team lacked visibility into whether data issues were silently cascading into dashboards, reports, and forecasts.

With a lean data engineering team and a centralized governance structure, the team needed a solution that could automate technical quality checks across Snowflake, catch silent errors like broken joins, delayed freshness, or schema changes, and prioritize fixes based on business impact. Without observability, Snowflake costs could quickly spiral without delivering trusted insights to the business.

From Setup to Insight: Etam Deployed Intelligent Data Monitoring in Days

Sifflet helped Etam layer intelligent monitoring across their dbt and Snowflake environment in days. Starting with out-of-the-box templates and evolving toward custom observability rules, the data team now monitors freshness, volume, distribution, and schema drift automatically.

Key setup features:

• Plug-and-play deployment: full environment monitored in a few days
• AI-powered detection: dynamic alert thresholds adjust to normal behavior
• GitHub-integrated tests: version-controlled rules alongside dbt models
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"Sifflet Makes Data Understandable for Everyone—Not Just Engineers" "With Sifflet, we went from zero to meaningful monitoring in just a few days. For a small team, that speed made all the difference."

Camille Maire, Head of Data Governance

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AI-Powered Monitoring Boosts Data Trust and Snowflake ROI at Scale

• Monitoring at scale: AI-driven alerts now flag volume and distribution anomalies without manual setup
• Faster time to insight: Technical issues surfaced proactively
• Optimizing ROI on Snowflake: Observability ensures the platform delivers business-ready data
• Roadmap to business logic: The team is now extending monitoring to cover rules like price integrity and stock accuracy