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.
Salma Bakouk, Co-founder and CEO of Sifflet, combines expertise in data pipelines and analytics with leadership honed as an Executive Director at Goldman Sachs. Salma is dedicated to building trust in data for her clients and advancing Sifflet’s position in the industry.
Wissem Fathallah, Co-founder and CPO of Sifflet, is a seasoned tech executive with a background in computer science from Centrale Paris. He has led high-impact engineering teams and built scalable data solutions at companies like Uber and Amazon. Wissem is passionate about new technologies, data and AI infrastructure, driving Sifflet’s mission to deliver reliable and actionable data insights.
Wajdi Fathallah, Co-founder and CTO of Sifflet, is a data and technology leader with a background in data science and a focus on scaling innovative solutions. He has held key roles at companies like Dashlane, SNCF, and Crédit Agricole, driving machine learning initiatives and revenue growth. Wajdi is committed to helping businesses unlock the full potential of their data through observability and operational excellence.
Join Our Team
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Frequently asked questions
What are some key benefits of using an observability platform like Sifflet?
Using an observability platform like Sifflet brings several benefits: real-time anomaly detection, proactive incident management, improved SLA compliance, and better data governance. By combining metrics, metadata, and lineage, we help teams move from reactive data quality monitoring to proactive, scalable observability that supports reliable, data-driven decisions.
How has AI changed the way companies think about data quality monitoring?
AI has definitely raised the stakes. As Salma shared on the Joe Reis Show, executives are being asked to 'do AI,' but many still struggle with broken pipelines. That’s why data quality monitoring and robust data observability are now seen as prerequisites for scaling AI initiatives effectively.
How can I measure the ROI of a data observability platform?
You can measure the ROI of a data observability platform by tracking key metrics like the number of data incidents per year, time to detection, and time to resolution. These real-time metrics give you insight into how often issues occur and how quickly your team can resolve them. Don’t forget to factor in qualitative benefits too, like improved team satisfaction and stronger data governance.
What role does Sifflet play in Etam’s data governance efforts?
Sifflet supports Etam by embedding data governance into their workflows through automated monitoring, anomaly detection, and data lineage tracking. This gives the team better visibility into their data pipelines and helps them troubleshoot issues quickly without slowing down innovation.
How does field-level lineage improve root cause analysis in observability platforms like Sifflet?
Field-level lineage allows users to trace issues down to individual columns across tables, making it easier to pinpoint where a problem originated. This level of detail enhances root cause analysis and impact assessment, helping teams resolve incidents quickly and maintain trust in their data.
Can I use data monitoring and data observability together?
Absolutely! In fact, data monitoring is often a key feature within a broader data observability solution. At Sifflet, we combine traditional monitoring with advanced capabilities like data profiling, pipeline health dashboards, and data drift detection so you get both alerts and insights in one place.
How does Sifflet help identify performance bottlenecks in dbt models?
Sifflet's dbt runs tab offers deep insights into model execution, cost, and runtime, making it easy to spot inefficiencies. You can also use historical performance data to set up custom dashboards and proactive monitors. This helps with capacity planning and ensures your data pipelines stay optimized and cost-effective.
What’s new in Sifflet’s data quality monitoring capabilities?
We’ve rolled out several powerful updates to help you monitor data quality more effectively. One highlight is our new referential integrity monitor, which ensures logical consistency between tables, like verifying that every order has a valid customer ID. We’ve also enhanced our Data Quality as Code framework, making it easier to scale monitor creation with templates and for-loops.
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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