Telemetry platform for mission-critical machines and complex systems
Sift builds a telemetry and monitoring platform designed for engineering teams operating systems too complex for manual oversight—spacecraft, autonomous vehicles, robotics. The stack reveals a data-intensive architecture (Kafka, Redpanda, Apache Flink, Druid, Pinot, Timescale) paired with advanced visualization (React, D3.js, WebGL, ECharts), suggesting they're solving both ingest-at-scale and sensemaking-at-scale problems. Hiring velocity is accelerating with a 14-to-8 engineering-to-sales ratio, while active projects signal geographic expansion (European playbook, go-to-market motion) and internal scaling (infrastructure, cost efficiency, automation).
Notable leadership hires: Sales Ops Lead
Sift provides a unified platform for ingesting, storing, and analyzing telemetry data from complex machines and systems. The product targets aerospace, defense, robotics, and autonomous vehicle engineering teams who need to detect unusual conditions automatically and visualize massive data volumes in real time. Founded in 2022 from challenges encountered in spaceflight, the company operates at 51–200 employees across the United States and United Kingdom, with active hiring in engineering, sales, and design roles. Core capability areas span data ingestion (Kafka/Redpanda), stream processing (Flink), analytical storage (Druid/Pinot), and interactive visualization, enabling engineers to close the feedback loop between development and production.
Sift's stack includes Kafka and Redpanda for event streaming, Apache Flink for stream processing, Druid and Pinot for analytical storage, PostgreSQL for relational data, and React, D3.js, WebGL, and ECharts for visualization. Infrastructure runs on AWS, Azure, and GCP with Kubernetes and Terraform.
Sift is headquartered in El Segundo, California. The company was founded in 2022 and is privately held with 51–200 employees.
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Sift'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.