Professional Background and Education
Salma Bakouk is the Chief Executive Officer and co-founder of Sifflet, a Paris-based company specializing in full data stack observability with an emphasis on data quality, reliability, and lineage. Sifflet’s platform targets both data engineers and business users by providing actionable insights to enhance trust in data, a critical factor for AI readiness and operational decision-making.
Ms. Bakouk’s academic credentials include a Master of Engineering degree in Computational and Applied Mathematics from CentraleSupélec (formerly École Centrale Paris), complemented by a Master’s degree in Statistics and Data Science from ENSAE Paris. This strong technical foundation underpins her leadership in the data observability domain.
Before founding Sifflet, Salma held a senior leadership role as Executive Director at Goldman Sachs in Sales & Trading, based in Hong Kong (2015–2020). At Goldman Sachs, she led teams implementing systematic trading strategies utilizing data and AI, providing her with significant industry experience at the intersection of finance, data science, and artificial intelligence.
Company Overview and Market Positioning
Founded in 2021 by Salma Bakouk alongside Wissem and Wajdi Fathallah, Sifflet has rapidly positioned itself as a leading end-to-end data observability platform. The startup is focused on solving persistent data quality issues by automating data monitoring and improving trustworthiness across data stacks, a feature increasingly prioritized due to the rise of AI applications and data-driven decision making.
Sifflet has successfully raised substantial venture funding rounds:
- A €12 million Series A round in March 2023, supported by investors including Bessemer Venture Partners.
- A subsequent $18 million capital raise in June 2025, with contributions from Capmont Technology, EQT Ventures, and Mangrove Capital Partners.
This capital infusion demonstrates strong investor confidence and supports the company’s acceleration of go-to-market activities and platform development.
Thought Leadership and Industry Influence
Salma Bakouk actively contributes to the discourse on data observability and its critical role in AI and data-driven enterprises. She has featured in multiple interviews and podcasts, such as the Joe Reiss Show (April 2025), where she elaborated on the evolution of the data observability space and the influence of AI in amplifying data trust as a bottom-line business metric.
Her leadership style combines her quantitative background and financial sector experience with a mission-driven approach focused on transforming data quality into a scalable, business-aligned discipline rather than a siloed data engineering challenge.
In recognition of her influence, Salma was named among the Most Influential CEOs in 2023 by CEO Monthly, reflecting her impact in the data technology ecosystem.
Strategic Insights Relevant to Engagement
- Salma’s engineering and mathematical expertise, paired with her financial industry leadership, suggests a data- and metrics-driven approach to strategic decisions and technical product focus. This indicates receptivity to solutions grounded in quantitative rigor and scalable analytics.
- Sifflet’s recent funding rounds signal an active growth phase, with potential budget and initiatives focused on expanding platform capabilities, market reach, and integration with AI systems—opportunities aligned with offerings that enhance data trustworthiness and operational observability.
- Salma’s public engagements and thought leadership emphasize AI readiness and enterprise data reliability as core priorities, making solutions that address data pipeline transparency, anomaly detection, and real-time analytics highly relevant.
- The company’s investor base—EQT Ventures, Bessemer, Mangrove Capital Partners—positions Sifflet within a network of high-growth, innovation-centric firms, likely favoring partners that can scale alongside their evolving platform.
LinkedIn Profile: [Salma Bakouk](http://www.linkedin.com/in/salmabakouk)
Company Website: [Sifflet](https://www.siffletdata.com)
