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
Why is smart alerting important in data observability?
Smart alerting helps your team focus on what really matters. Instead of flooding your Slack with every minor issue, a good observability tool prioritizes alerts based on business impact and data asset importance. This reduces alert fatigue and ensures the right people get notified at the right time. Look for platforms that offer customizable severity levels, real-time alerts, and integrations with your incident management tools like PagerDuty or email alerts.
How does Sifflet help reduce alert fatigue in data teams?
Great question! Sifflet tackles alert fatigue by using AI-native monitoring that understands business context. Instead of flooding teams with false positives, it prioritizes alerts based on downstream impact. This means your team focuses on real issues, improving trust in your observability tools and saving valuable engineering time.
What is data governance and why does it matter for modern businesses?
Data governance is a framework of policies, roles, and processes that ensure data is accurate, secure, and used responsibly across an organization. It brings clarity and accountability to data management, helping teams trust the data they use, stay compliant with regulations, and make confident decisions. When paired with data observability tools, governance ensures data remains reliable and actionable at scale.
What’s the difference between data distribution and data lineage tracking?
Great distinction! Data distribution shows you how values are spread across a dataset, while data lineage tracking helps you trace where that data came from and how it’s moved through your pipeline. Both are essential for root cause analysis, but they solve different parts of the puzzle in a robust observability platform.
Can Sifflet Insights help with data pipeline monitoring?
Absolutely! Sifflet Insights connects to your broader observability platform, giving you visibility into data pipeline health right from your BI dashboards. It helps track incidents, monitor data freshness, and detect anomalies before they impact your business decisions.
What types of data lineage should I know about?
There are four main types: technical lineage, business lineage, cross-system lineage, and governance lineage. Each serves a different purpose, from debugging pipelines to supporting compliance. Tools like Sifflet offer field-level lineage for deeper insights, helping teams across engineering, analytics, and compliance understand and trust their data.
What is data observability and why is it important for modern data teams?
Data observability is the practice of monitoring data as it moves through your pipelines to detect, understand, and resolve issues proactively. It’s crucial because it helps data teams ensure data reliability, improve decision-making, and reduce the time spent firefighting data issues. With the growing complexity of data systems, having a robust observability platform is key to maintaining trust in your data.
What’s a real-world example of Dailymotion using real-time metrics to drive business value?
One standout example is their ad inventory forecasting tool. By embedding real-time metrics into internal tools, sales teams can plan campaigns more precisely and avoid last-minute scrambles. It’s a great case of using data to improve both accuracy and efficiency.
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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