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.

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

Frequently asked questions

How can I monitor the health of my ETL or ELT pipelines?

Monitoring pipeline health is essential for maintaining data reliability. You can use tools that offer data pipeline monitoring features such as real-time metrics, ingestion latency tracking, and pipeline error alerting. Sifflet’s pipeline health dashboard gives you full visibility into your ETL and ELT processes, helping you catch issues early and keep your data flowing smoothly.

How can I better manage stakeholder expectations for the data team?

Setting clear priorities and using a centralized pipeline orchestration visibility tool can help manage expectations across the organization. When stakeholders understand what the team can deliver and when, it builds trust and reduces pressure on your team, leading to a healthier and happier work environment.

Why is integration with my existing tools important for observability?

A good observability platform should fit right into your current stack. That means supporting tools like dbt, Airflow, and your cloud infrastructure. Seamless integration ensures better pipeline orchestration visibility and makes it easier to act on data issues without disrupting your workflows.

What are some key features to look for in an observability platform for data?

A strong observability platform should offer data lineage tracking, real-time metrics, anomaly detection, and data freshness checks. It should also integrate with your existing tools like Airflow or Snowflake, and support alerting through Slack or webhook integrations. These capabilities help teams monitor data pipelines effectively and respond quickly to issues.

How can inefficient SQL queries impact my data pipeline performance?

Great question! Inefficient SQL queries can lead to slow dashboards, increased ingestion latency, and even failed workloads. By optimizing your queries using best practices like proper filtering and avoiding SELECT *, you help improve data pipeline monitoring and maintain overall data reliability.

What’s the best way to prevent bad data from impacting our business decisions?

Preventing bad data starts with proactive data quality monitoring. That includes data profiling, defining clear KPIs, assigning ownership, and using observability tools that provide real-time metrics and alerts. Integrating data lineage tracking also helps you quickly identify where issues originate in your data pipelines.

Why is this integration important for data pipeline monitoring?

Bringing Sifflet’s observability tools into Apache Airflow allows for proactive data pipeline monitoring. You get real-time metrics, anomaly detection, and data freshness checks that help you catch issues early and keep your pipelines healthy.

What are some key benefits of using an observability platform like Sifflet?

Using an observability platform like Sifflet brings several benefits: real-time anomaly detection, proactive incident management, improved SLA compliance, and better data governance. By combining metrics, metadata, and lineage, we help teams move from reactive data quality monitoring to proactive, scalable observability that supports reliable, data-driven decisions.

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