Business-aware data observability platform powered by AI agents
Sifflet sells data observability that ties pipeline incidents to downstream business usage, with integrations spanning Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow and Fivetran. Its own stack of Java/Spring Boot, FastAPI, PyTorch and Temporal on Kubernetes points to an ML-driven detection engine. Current hiring is minimal and senior, focused on legal leadership and enterprise sales in France, which signals formalizing compliance ahead of larger deals.
Sifflet, founded in 2021 and headquartered in New York, builds a data observability platform for data engineers and data leaders. The product uses a set of AI agents to flag anomalies, trace root causes through lineage, and propose code fixes, ranking incidents by business risk. It connects to major warehouses, lakehouses, orchestration and BI tools, including Snowflake, BigQuery, Databricks, Airflow, Looker and Power BI. The privately held company has 51–200 employees and runs cloud infrastructure across AWS, GCP and Azure.
Sifflet runs Python, Java with Spring Boot, FastAPI, TypeScript and Vue, with PyTorch and Temporal, on Kubernetes and AWS EKS, plus GitLab CI/CD, Prometheus, Grafana, Loki and Sentry.
Sifflet is headquartered in New York, New York. Its current hiring is in France.
Sifflet is setting up and scaling a legal department, building a strategic sales territory across France, and putting scalable processes in place for high-volume enterprise deals.
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Sifflet's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.