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 role does data lineage play in incident management and alerting?
Data lineage provides visibility into data dependencies, which helps teams assign, prioritize, and resolve alerts more effectively. In an observability platform like Sifflet, this means faster incident response, better alert correlation, and improved on-call management workflows.
Why are data consumers becoming more involved in observability decisions?
We’re seeing a big shift where data consumers—like analysts and business users—are finally getting a seat at the table. That’s because data observability impacts everyone, not just engineers. When trust in data is operationalized, it boosts confidence across the business and turns data teams into value creators.
What kind of data quality monitoring does Sifflet offer when used with dbt?
When paired with dbt, Sifflet provides robust data quality monitoring by combining dbt test insights with ML-based rules and UI-defined validations. This helps you close test coverage gaps and maintain high data quality throughout your data pipelines.
How does Sifflet help with data observability during the CI process?
Sifflet integrates directly with your CI pipelines on platforms like GitHub and GitLab to proactively surface issues before code is merged. By analyzing the impact of dbt model changes and running data quality monitors in testing environments, Sifflet ensures data reliability and minimizes production disruptions.
How can data observability help companies stay GDPR compliant?
Great question! Data observability plays a key role in GDPR compliance by giving teams real-time visibility into where personal data lives, how it's being used, and whether it's being processed according to user consent. With an observability platform in place, you can track data lineage, monitor data quality, and quickly respond to deletion or access requests in a compliant way.
Why is semantic quality monitoring important for AI applications?
Semantic quality monitoring ensures that the data feeding into your AI models is contextually accurate and production-ready. At Sifflet, we're making this process seamless with tools that check for data drift, validate schema, and maintain high data quality without manual intervention.
What improvements has Sifflet made to incident management workflows?
We’ve introduced Augmented Resolution to help teams group related alerts into a single collaborative ticket, streamlining incident response. Plus, with integrations into your ticketing systems, Sifflet ensures that data issues are tracked, communicated, and resolved efficiently. It’s all part of our mission to boost data reliability and support your operational intelligence.
How does SQL Table Tracer support different SQL dialects for data lineage tracking?
SQL Table Tracer uses Antlr4 and a unified grammar with semantic predicates to support multiple SQL dialects like Snowflake, Redshift, and PostgreSQL. This ensures accurate data lineage tracking across diverse systems without needing separate parsers for each dialect.
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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