At Sifflet,
Data Means Business.
Data drives every strategic decision, guides innovation, and powers transformation. But how do companies ensure their data is reliable? How can they trust the insights that guide critical business choices? How do they turn raw information into actionable intelligence, high-performing products, and superior strategies? Enter Sifflet.
Who We Are
We are a data observability platform.
We offer end-to-end oversight into the entire data stack, helping teams to uncover, prevent and overcome the technical and organizational obstacles that get in the way of better quality, more reliable data.
Our Mission
We help companies see data breakthroughs. Sifflet delivers smoother running data stacks by providing detailed oversight and solutions that reduce data breaks, improve team alignment and operations, and build confidence in the numbers. The result? Superior insights, value and products from data.
Meet our Executive team
Sifflet was built by a data-obsessed team for
data-obsessed teams.
Before founding Sifflet, Salma worked in quantitative sales & trading at Goldman Sachs, where she saw firsthand how unreliable data could undermine even the most sophisticated models. She holds two master’s degrees in Applied Mathematics and Computer Science from École Centrale Paris. Named among Europe’s Top 100 Women in Tech, Salma is a frequent speaker at leading industry events including Gartner D&A Summit and Big Data LDN. Outside of work, she loves running mountain trails, discovering new cities, and spending time with her dog always chasing the same clarity and balance she strives to bring to data.
Joe is Head of Sales at Sifflet, focused on solving the data trust problem by helping teams detect broken data, understand business impact, and fix issues before they drive bad decisions. Previously at Matillion for 9 years, he led enterprise and strategic sales across EMEA, built and scaled high performing teams, consistently outperformed targets, and started as the company’s first sales hire, helping shape early go to market and partnerships.
Rémi is Head of Operations at Sifflet where he drives operational execution and scale. Previously at Contentsquare for nearly a decade, he led strategic cross functional projects and built operational excellence capabilities, spanning BI and KPIs, data governance and master data, knowledge management, tooling, process optimization, and PMO leadership.
Alex is Head of Solutions Engineering and Customer Success at Sifflet, leading technical presales and post sales to drive smooth adoption and measurable outcomes. Previously at Deepomatic, he built and scaled Sales Engineering from first hire to Director, defined sales methodology with leadership, shaped the roadmap with product, built key partnerships, signed the company’s first North America customer, and expanded into LATAM. Earlier, he was a data and analytics consultant at Accenture in the UK, delivering BI and reporting programs and leading agile project work.
Romain Doutriaux is Head of Marketing at Sifflet, driving brand and pipeline with a sharp go to market lens. Previously, he led global marketing at Pigment, scaling inbound pipe gen, ABX and influence plays, and a 20 plus person team. Before that, he spent over seven years at Dataiku, moving from France Marketing Manager to VP EMEA Marketing, owning EMEA strategy across PR, digital, events, ABM, partnerships, and positioning in tight alignment with Sales and Product.
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Frequently asked questions
How does Sifflet help with data drift detection in machine learning models?
Great question! Sifflet's distribution deviation monitoring uses advanced statistical models to detect shifts in data at the field level. This helps machine learning engineers stay ahead of data drift, maintain model accuracy, and ensure reliable predictive analytics monitoring over time.
Why is a user-friendly interface important in an observability tool?
A user-friendly interface boosts adoption across teams and makes it easier to navigate complex datasets. For observability tools, especially those focused on data cataloging and data discovery, a clean UI enables faster insights and more efficient collaboration.
What exactly is data observability, and how is it different from traditional data monitoring?
Great question! Data observability goes beyond traditional data monitoring by not only detecting when something breaks in your data pipelines, but also understanding why it matters. While monitoring might tell you a pipeline failed, data observability connects that failure to business impact—like whether your CFO’s dashboard is now showing outdated numbers. It's about trust, context, and actionability.
Can non-technical users benefit from Sifflet’s Data Catalog?
Yes, definitely! Sifflet is designed to be user-friendly for both technical and business users. With features like AI-driven description recommendations and easy-to-navigate asset pages, even non-technical users can confidently explore and understand the data they need.
How does Sifflet support collaboration across data teams?
Sifflet promotes un-siloed data quality by offering a unified platform where data engineers, analysts, and business users can collaborate. Features like pipeline health dashboards, data lineage tracking, and automated incident reports help teams stay aligned and respond quickly to issues.
How can data observability help reduce data entropy?
Data entropy refers to the chaos and disorder in modern data environments. A strong data observability platform helps reduce this by providing real-time metrics, anomaly detection, and data lineage tracking. This gives teams better visibility across their data pipelines and helps them catch issues early before they impact the business.
What is data observability and why is it important?
Data observability is the ability to monitor, understand, and troubleshoot data systems using real-time metrics and contextual insights. It's important because it helps teams detect and resolve issues quickly, ensuring data reliability and reducing the risk of bad data impacting business decisions.
How does schema evolution impact batch and streaming data observability?
Schema evolution can introduce unexpected fields or data type changes that disrupt both batch and streaming data workflows. With proper data pipeline monitoring and observability tools, you can track these changes in real time and ensure your systems adapt without losing data quality or breaking downstream processes.
Still have questions?
Want to join the team?
We're seeking driven individuals eager to roll up their sleeves and help make data observability everyone's business.
Make Data Observability Everyone’s Business
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