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Staff Software Engineer, Data

principalremoteMarina Del Rey, CA, USСкор undefined/1002нед назад
Аналитика рынка
📊 Data Engineer: зарплаты и спрос на рынке
Стек
argocdclouddevopsflinkkafkakubernetespostgresqlprometheuspythonsqlterraformtimescaledb
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Описание
About Sift Sift is the data infrastructure platform for hardware engineering teams. Sift turns high-frequency telemetry into engineering insights for mission-critical systems. Teams use Sift to build and operate rockets, satellites, autonomous vehicles, energy systems, defense platforms, and more. Founded by former SpaceX engineers who built the tools behind Dragon and Starlink, Sift is building the data infrastructure to herald the AI era for physical systems. In This Role, You'll: Design and build a horizontally scalable platform for ingesting millions of hardware sensor data points per second Develop durable, efficient database solutions to support real-time reads and large-scale analytics workloads Pioneer data architecture by integrating recent innovations in streaming and storage, including cloud-native and diskless designs Help define engineering culture, standards, and processes Collaborate closely with peers on architecture, design, code reviews, and scaling strategies The Skillset You'll Bring: Bachelor's degree in Computer Science, Engineering, Physics, or another STEM discipline 7+ years of experience in backend, infrastructure, or data engineering roles Hands-on experience with event-time-based stream processing or streaming SQL systems using tools like Apache Flink, Kafka Streams, Beam, or similar Proficiency with relational and time-series databases like PostgreSQL, Druid, Pinot, TimescaleDB, or equivalent Experience with large-scale distributed systems or low-latency backend services, ideally written in Go, Rust, or Python Familiarity with DevOps and cloud infrastructure tools such as Kubernetes, Prometheus, ArgoCD, and Terraform Strong communication skills and a collaborative approach to problem-solving Bonus Points: Familiarity with telemetry data from hardware systems, high-throughput ingest pipelines, or columnar storage formats like Apache Arrow and Parquet Experience building resilient, performant systems that scale to billions of records Curio
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