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
.avif)
Frequently asked questions
What makes Sifflet’s data lineage tracking stand out?
Sifflet offers one of the most advanced data lineage tracking capabilities out there. Think of it like a GPS for your data pipelines—it gives you full traceability, helps identify bottlenecks, and supports better pipeline orchestration visibility. It's a game-changer for data governance and optimization.
Can Sifflet detect anomalies in my data pipelines?
Yes, it can! Sifflet uses machine learning for anomaly detection, helping you catch unexpected changes in data volume or quality. You can even label anomalies to improve the model's accuracy over time, reducing alert fatigue and improving incident response automation.
What makes Sifflet's approach to data quality unique?
At Sifflet, we believe data quality isn't one-size-fits-all. Our observability platform blends technical robustness with business context, offering customized data quality monitoring that adapts to your specific use cases. This means you get both reliable pipelines and meaningful metrics that align with your business goals.
How does Full Data Stack Observability help improve data quality at scale?
Full Data Stack Observability gives you end-to-end visibility into your data pipeline, from ingestion to consumption. It enables real-time anomaly detection, root cause analysis, and proactive alerts, helping you catch and resolve issues before they affect your dashboards or reports. It's a game-changer for organizations looking to scale data quality efforts efficiently.
Can Sifflet help me monitor data drift and anomalies beyond what dbt offers?
Absolutely! While dbt is fantastic for defining tests, Sifflet takes it further with advanced data drift detection and anomaly detection. Our platform uses intelligent monitoring templates that adapt to your data’s behavior, so you can spot unexpected changes like missing rows or unusual values without setting manual thresholds.
How do Service Level Indicators (SLIs) help improve data product reliability?
SLIs are a fantastic way to measure the health and performance of your data products. By tracking metrics like data freshness, anomaly detection, and real-time alerts, you can ensure your data meets expectations and stays aligned with your team’s SLA compliance goals.
Why is data observability becoming essential for data-driven companies?
As more businesses rely on data to drive decisions, ensuring data reliability is critical. Data observability provides transparency into the health of your data assets and pipelines, helping teams catch issues early, stay compliant with SLAs, and ultimately build trust in their data.
What is data observability and why is it important for modern data teams?
Data observability is the ability to monitor and understand the health of your data across the entire data stack. As data pipelines become more complex, having real-time visibility into where and why data issues occur helps teams maintain data reliability and trust. At Sifflet, we believe data observability is essential for proactive data quality monitoring and faster root cause analysis.
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
PRODUCT
-p-500.png)
