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.

Join Our Team

Apply

Frequently asked questions

Can SQL Table Tracer be used to improve incident response and debugging?

Absolutely! By clearly mapping upstream and downstream table relationships, SQL Table Tracer helps teams quickly trace issues back to their source. This accelerates root cause analysis and supports faster, more effective incident response workflows in any observability platform.

Can I add non-integrated tools like Salesforce or HubSpot to my data catalog?

Absolutely! With Sifflet’s declarative framework, you can programmatically declare assets from tools like Salesforce, SAP, or HubSpot, even if they aren’t natively integrated. This helps you maintain a complete and unified view of your data ecosystem for better data governance.

What’s the main difference between ETL and ELT?

Great question! While both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration methods, the key difference lies in the order of operations. ETL transforms data before loading it into a data warehouse, whereas ELT loads raw data first and transforms it inside the warehouse. ELT has become more popular with the rise of cloud data warehouses like Snowflake and BigQuery, which offer scalable storage and computing power. If you're working with large volumes of data, ELT might be the better fit for your data pipeline monitoring strategy.

How does Sifflet help reduce alert fatigue in data observability?

Sifflet uses AI-driven context and dynamic thresholding to prioritize alerts based on impact and relevance. Its intelligent alerting system ensures users only get notified when it truly matters, helping reduce alert fatigue and enabling faster, more focused incident response.

How can I monitor the health of my ingestion pipelines?

To keep your ingestion pipelines healthy, it's best to use observability tools that offer features like pipeline health dashboards, data quality monitoring, and anomaly detection. These tools provide visibility into data flow, alert you to schema drift, and help with root cause analysis when issues arise.

How can data observability support the implementation of a Single Source of Truth?

Data observability helps validate and sustain a Single Source of Truth by proactively monitoring data quality, tracking data lineage, and detecting anomalies in real time. Tools like Sifflet provide automated data quality monitoring and root cause analysis, which are essential for maintaining trust in your data and ensuring consistent decision-making across teams.

How does the Sifflet AI Assistant improve data observability at scale?

The Sifflet AI Assistant enhances data observability by automatically fine-tuning your monitoring setup using machine learning and dynamic thresholds. It continuously adapts to changes in your data pipelines, reducing false positives and ensuring accurate anomaly detection, even as your data scales globally.

How does Sifflet help with anomaly detection in data pipelines?

Sifflet uses machine learning to power anomaly detection across your data ecosystem. Instead of relying on static rules, it learns your data’s patterns and flags unusual behavior—like a sudden drop in transaction volume. This helps teams catch issues early, avoid alert fatigue, and focus on incidents that actually impact business outcomes. It’s data quality monitoring with real intelligence.

Read all FAQs

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.

Join Sifflet

At Sifflet, Data Means Business.