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.
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.
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.
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.
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.
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.
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.
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
What role does data lineage tracking play in data governance?
Data lineage tracking is essential for understanding where data comes from, how it changes, and where it goes. It supports compliance efforts, improves root cause analysis, and reduces confusion in cross-functional teams. Combined with data governance, lineage tracking ensures transparency in data pipelines and builds trust in analytics and reporting.
What are the main challenges of implementing Data as a Product?
Some key challenges include ensuring data privacy and security, maintaining strong data governance, and investing in data optimization. These areas require robust monitoring and compliance tools. Leveraging an observability platform can help address these issues by providing visibility into data lineage, quality, and pipeline performance.
Why did jobvalley choose Sifflet over other data catalog vendors?
After evaluating several data catalog vendors, jobvalley selected Sifflet because of its comprehensive features that addressed both data discovery and data quality monitoring. The platform’s ability to streamline onboarding and support real-time metrics made it the ideal choice for their growing data team.
Why is data lineage tracking important for governance in a hybrid architecture?
Data lineage tracking provides transparency into how data moves and transforms across systems. In hybrid architectures, it helps enforce governance by showing where data comes from, who owns it, and how changes impact downstream consumers, making compliance and audit logging much easier.
Can observability tools help with GDPR-related incident response?
Absolutely! Observability tools can support GDPR compliance by enabling faster incident response automation. If there's a data breach, you need to notify users and authorities within 72 hours. Real-time alerts, telemetry instrumentation, and logs management help your team detect issues quickly, understand the impact, and take action to stay compliant.
How does the Model Context Protocol (MCP) improve data observability with LLMs?
Great question! MCP allows large language models to access structured external context like pipeline metadata, logs, and diagnostics tools. At Sifflet, we use MCP to enhance data observability by enabling intelligent agents to monitor, diagnose, and act on issues across complex data pipelines in real time.
What does a modern data stack look like and why does it matter?
A modern data stack typically includes tools for ingestion, warehousing, transformation and business intelligence. For example, you might use Fivetran for ingestion, Snowflake for warehousing, dbt for transformation and Looker for analytics. Investing in the right observability tools across this stack is key to maintaining data reliability and enabling real-time metrics that support smart, data-driven decisions.
What are some common reasons data freshness breaks down in a pipeline?
Freshness issues often start with delays in source systems, ingestion bottlenecks, slow transformation jobs, or even caching problems in dashboards. That's why a strong observability platform needs to monitor every stage of the pipeline, from ingestion latency to delivery, to ensure data reliability and timely decision-making.
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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