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Technical Teams

Data Engineer

You’ll be the boss. Sifflet gives you the capabilities and oversight to manage your data stack like never before, faster than you ever thought possible.

Launch Product Tour

Troubleshoot and Debug

Sifflet makes troubleshooting and debugging faster, more efficient and more effective thanks to pipeline failure or data anomaly alerts and rich contextual information.

Pipeline Performance Optimization

Pipelines power your data stack. Sifflet helps you monitor pipeline performance and get insight into bottlenecks and inefficient transformations.

Quality Assurance

Uplevel your data quality thanks to automated quality checks and validations and custom rules to ensure data integrity.

More Productive. More Powerful.

Sifflet augments your productivity by giving you end-to-end visibility into your architecture, assets, and pipelines. AI-powered monitoring sends you the right alerts, at the right time, so you can triage efficiently and effectively. And advanced lineage capabilities enable you to get to resolution faster.

Built for Business.

Sifflet helps you collaborate better with users on the business end. Give your data consumers self-serve tools, such as smart monitoring setup that leverages large language models and embed monitoring alerts into their data products.

See Value From Day One.

Sifflet connects to hundreds of tools already in your stack and offers out of the box monitors and tooling so you can start seeing value from day one.

Sifflet’s AI Helps Us Focus on What Moves the Business

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Enabler of Cross Platform Data Storytelling"

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Building Harmony Between Data and Business With Sifflet"

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

" Sifflet empowers our teams through Centralized Data Visibility"

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet allows us to find and trust our data"

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi

CTO, Carrefour Links

"A core component of our data strategy and transformation"

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman

Director of Data, Hypebeast

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Frequently asked questions

What kind of real-time alerts can I expect with Sifflet and dbt together?

With Sifflet and dbt working together, you get real-time alerts delivered straight to your favorite tools like Slack, Microsoft Teams, or email. Whether a dbt test fails or a data anomaly is detected, your team will be notified immediately, helping you respond quickly and maintain data quality monitoring at all times.

How does Sifflet help with root cause analysis when something breaks in a data pipeline?

When a data issue arises, Sifflet gives you the context you need to act fast. Our observability platform connects the dots across your data stack—tracking lineage, surfacing schema changes, and highlighting impacted assets. That makes root cause analysis much easier, whether you're dealing with ingestion latency or a failed transformation job. Plus, our AI helps explain anomalies in plain language.

What is SQL Table Tracer and how does it help with data observability?

SQL Table Tracer (STT) is a lightweight library that extracts table-level lineage from SQL queries. It plays a key role in data observability by identifying upstream and downstream tables, making it easier to understand data dependencies and track changes across your data pipelines.

How does the shift to poly cloud impact observability platforms?

The move toward poly cloud environments increases the complexity of monitoring, but observability platforms are evolving to unify insights across multiple cloud providers. This helps teams maintain SLA compliance, monitor ingestion latency, and ensure data reliability regardless of where workloads are running.

Why is standardization important when scaling dbt, and how does Sifflet support it?

Standardization is key to maintaining control as your dbt project grows. Sifflet supports this by centralizing metadata and enabling compliance monitoring through features like data contracts enforcement and asset tagging. This ensures consistency, improves data governance, and reduces the risk of data drift or unmonitored critical assets.

What features should we look for in a data observability tool?

A great data observability tool should offer automated data quality checks like data freshness checks and schema change detection, field-level data lineage tracking for root cause analysis, and a powerful metadata search engine. These capabilities streamline incident response and help maintain data governance across your entire stack.

Why is data observability so important for AI and analytics initiatives?

Great question! Data observability ensures that the data fueling AI and analytics is reliable, accurate, and fresh. At Sifflet, we see data observability as both a technical and business challenge, which is why our platform focuses on data quality monitoring, anomaly detection, and real-time metrics to help enterprises make confident, data-driven decisions.

Can classification tags improve data pipeline monitoring?

Absolutely! By tagging fields like 'Low Cardinality', data teams can quickly identify which fields are best suited for specific monitors. This enables more targeted data pipeline monitoring, making it easier to detect anomalies and maintain SLA compliance across your analytics pipeline.

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