How does Sifflet support collaboration across data teams?

Sifflet promotes un-siloed data quality by offering a unified platform where data engineers, analysts, and business users can collaborate. Features like pipeline health dashboards, data lineage tracking, and automated incident reports help teams stay aligned and respond quickly to issues.

What role does data lineage tracking play in managing complex dbt pipelines?

Data lineage tracking is essential when your dbt projects grow in size and complexity. Sifflet provides a unified, metadata-rich lineage graph that spans your entire data stack, helping you quickly perform root cause analysis and impact assessments. This visibility is crucial for maintaining trust and transparency in your data pipelines.

What role does data governance play in a data observability platform?

Data governance is a core component of any robust data observability solution. Look for platforms that offer features like audit logging, access controls, and encryption. These capabilities help ensure your organization stays compliant with regulations like GDPR, while also protecting sensitive data and maintaining transparency across teams.

Why is data freshness so important for data reliability?

Great question! Data freshness is a key part of data reliability because decisions are only as good as the data they're based on. If your data is outdated or delayed, it can lead to flawed insights and missed opportunities. That's why data freshness checks are a foundational element of any strong data observability strategy.

What makes Datadog and Splunk suitable for real-time data observability?

Both Datadog and Splunk excel at real-time telemetry instrumentation. They capture logs, metrics, and traces across applications, pipelines, and infrastructure. This real-time detection and unified observability platform make them great for environments where data reliability depends on fast incident detection and root cause analysis.

Why is agentic observability critical for modern data environments?

Modern data environments are complex, distributed, and constantly evolving. Agentic observability is essential because it brings AI-powered automation to the forefront, enabling proactive monitoring, anomaly detection, and dynamic thresholding. It's a scalable approach to managing data drift detection, pipeline health, and incident response in real time.

Is data observability relevant for small businesses?

If you really need data observability.

Is this feature scalable for large datasets and multiple data assets?

Yes, it is! With Sifflet's auto-coverage and observability tools, you can monitor distribution deviation at scale with just a few clicks. Whether you're working with batch data observability or streaming data monitoring, Sifflet has you covered with automated, scalable insights.

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.

We’re Hiring!

Chief Executive Officer

Salma Bakouk

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.

Head of Sales

Joe Steadman

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.

Head of Operations

Rémi Bastien

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.

Head of Product

Laura Malins

Laura Malins is the Head of Product at Sifflet. She spent a decade at Matillion, joining when the company was around 10 people and helping drive its growth to unicorn scale, including leading major product launches, evolving pricing and billing, optimising GTM approaches and improving the customer onboarding journey. She later led product at ALTR where she drove forwards a comprehensive vision and more complete product processes. Laura is passionate about building great products and supports individuals and small companies through board roles and mentoring.

Head of Solution Engineering

Alex Iorga

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.

Head of Marketing

Romain Doutriaux

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.

Head of Engineering

Benoit Faucon

Benoît Faucon is the head of Engineering at Sifflet, leading integrations and infrastructure. Previously, he held lead infrastructure and security roles at Terality and Mindsay, where he built reliable cloud platforms, improved developer velocity, and drove security and compliance efforts, including SOC 2 readiness. Earlier in his career at Withings, he delivered automation and observability systems across large scale bare metal and cloud environments. He is a graduate of Ecole Centrale Paris.

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Frequently asked questions

Can observability platforms help AI systems make better decisions with data?

Absolutely. AI systems need more than just schemas—they need context. Observability platforms like Sifflet provide machine-readable trust signals, data freshness checks, and reliability scores through APIs. This allows autonomous agents to assess data quality in real time and make smarter decisions without relying on outdated documentation.

What role does data lineage tracking play in volume monitoring?

Data lineage tracking is essential for root cause analysis when volume anomalies occur. It helps you trace where data came from and how it's been transformed, so if a volume drop happens, you can quickly identify whether it was caused by a failed API, upstream filter, or schema change. This context is key for effective data pipeline monitoring.

Can data quality monitoring alone guarantee data reliability?

Not quite. While data quality monitoring helps ensure individual datasets are accurate and consistent, data reliability goes further by ensuring your entire data system is dependable over time. That includes pipeline orchestration visibility, anomaly detection, and proactive monitoring. Pairing data quality with a robust observability platform gives you a more comprehensive approach to reliability.

How does data observability complement a data catalog?

While a data catalog helps you find and understand your data, data observability ensures that the data you find is actually reliable. Observability tools like Sifflet monitor the health of your data pipelines in real time, using features like data freshness checks, anomaly detection, and data quality monitoring. Together, they give you both visibility and trust in your data.

Why is metadata so important for modern data monitoring?

Great question! Metadata adds the context that traditional monitoring lacks. It helps you understand not just what failed, but also where, why, and who owns it. By layering in technical, operational, and business metadata, your data monitoring becomes smarter and more actionable—making it easier to maintain data quality and reliability across your stack.

What role does metadata play in a data observability platform?

Metadata provides context about your data, such as who created it, when it was modified, and how it's classified. In a data observability platform, strong metadata management enhances data discovery, supports compliance monitoring, and ensures consistent, high-quality data across systems.

What makes data observability different from traditional monitoring tools?

Traditional monitoring tools focus on infrastructure and application performance, while data observability digs into the health and trustworthiness of your data itself. At Sifflet, we combine metadata monitoring, data profiling, and log analysis to provide deep insights into pipeline health, data freshness checks, and anomaly detection. It's about ensuring your data is accurate, timely, and reliable across the entire stack.

What’s the difference between technical and business data quality?

That's a great distinction to understand! Technical data quality focuses on things like accuracy, completeness, and consistency—basically, whether the data is structurally sound. Business data quality, on the other hand, asks if the data actually supports how your organization defines success. For example, a report might be technically correct but still misleading if it doesn’t reflect your current business model. A strong data governance framework helps align both dimensions.

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At Sifflet, Data Means Business.