Sifflet Tech Stack

AI-powered data observability platform that links data quality to business impact

Software Development New York, New York 51–200 employees Founded 2021 Privately Held

Sifflet operates a data observability platform centered on detecting anomalies, diagnosing root causes, and suggesting code fixes via AI agents—Sentinel, Sage, and Forge. The tech stack (Java/Spring/Python/FastAPI/PyTorch/Temporal) reveals a production ML infrastructure; active projects around LLM integration, lineage modeling, and automated profiling confirm heavy algorithmic investment. The hiring pattern—7 engineering roles across senior, mid, and staff levels, mostly filled in the last month—signals the company is scaling the core platform while technical debt and ingestion query costs remain active friction points.

Tech Stack 29 technologies

Core StackJava Python FastAPI PyTorch Temporal TypeScript Vue Kubernetes GitLab CI/CD Prometheus Loki Grafana Sentry Snowflake BigQuery Looker Power BI dbt Fivetran Apache Airflow MySQL AWS AWS RDS Spring Boot Amazon Redshift Amazon QuickSight AWS EKS GCP Azure

What Sifflet Is Building

◆Challenges

Troubleshooting data quality issues
Monitoring massive data sets
Technical debt
Scaling needs
Optimizing ingestion queries cost
Modularizing integration architecture
Reducing alerting noise
Identifying root cause of data anomalies
Reducing ingestion engine cost
Scaling ingestion engine

▲Active Projects

Query history root cause analysis
Automated data profiling and anomaly detection
Lineage model development
Ingestion query optimization
Automated monitoring at scale
New data quality checks
Llm and machine learning features in production
Single-tenant architecture implementation
Build new lineage model
Optimize ingestion engine queries

Hiring Activity

Accelerating10 roles · 8 in 30d

Department

Engineering

Marketing

Seniority

Senior

Mid

Staff

Lead

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About Sifflet

Sifflet is a data observability platform built for data engineering teams and leaders at mid-to-large enterprises. The platform shifts teams from reactive incident response to proactive risk assessment by enriching technical data quality alerts with full-stack lineage and downstream business usage context. Rather than flag alerts by technical severity alone, Sifflet prioritizes incidents based on business impact—a critical distinction for teams managing data dependencies across BI, analytics, and AI pipelines. The company operates across North America and France, with an engineering-focused organizational structure supporting active development in anomaly detection, lineage analysis, and ingestion optimization.

HeadquartersNew York, New York

Company Size51–200 employees

Founded2021

Hiring MarketsFrance

Frequently Asked Questions

What tech stack does Sifflet use?

Sifflet runs on Java, Spring Boot, Python, FastAPI, and PyTorch for the core platform. Infrastructure: Kubernetes, AWS (EKS, RDS), GCP, Azure. Observability: Prometheus, Loki, Grafana. Data warehouse integrations: Snowflake, BigQuery, Redshift. Orchestration: Temporal, Apache Airflow.

What is Sifflet working on?

Active projects include LLM and ML features in production, query history root cause analysis, automated data profiling and anomaly detection, lineage model development, single-tenant architecture, and ingestion query optimization to reduce platform costs.