Galileo 🔭

Software Engineer, Platform and Data Infrastructure

Galileo 🔭

full-time

Posted on:

Origin:  • 🇺🇸 United States • California

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Salary

💰 $180,000 - $300,000 per year

Job Level

Mid-LevelSenior

Tech Stack

Distributed SystemsGrafanaKafkaKubernetesNoSQLPythonRabbitMQSparkSQL

About the role

  • Build and scale core infrastructure – design and optimize distributed systems and APIs that handle millions of real-time queries with low latency and high reliability
  • Develop data-intensive systems – work across SQL, NoSQL, time-series, and object stores, ensuring data pipelines and lookups are optimized for throughput and efficiency
  • Optimize performance at scale – profile and tune systems for latency, throughput, and cost
  • Work on real-time serving systems – design high-throughput caching layers and data lookup services
  • Build and extend pub-sub and orchestration frameworks – leverage and improve systems like Kafka, RabbitMQ, and Celery to coordinate workloads and streaming data pipelines
  • Design internal developer tooling – create load testing, benchmarking, and performance analysis tools
  • Collaborate cross-functionally – partner with product, research, and application engineering teams to support new capabilities, models, and workloads

Requirements

  • Experience with large scale distributed systems
  • Addressed challenges that come with systems of scale
  • Worked on and optimized high throughput traffic on SQL and NoSQL data stores, time-series databases and/or Object stores
  • Experience with NoSQL databases and time-series databases
  • Worked on real-time high throughput caching systems
  • Excellent python programming skills
  • Worked with raw-data lookup indexes such as Lucene and/or similar frameworks
  • Experience with runtime orchestration and pub-sub frameworks like RabbitMQ, Celery, Kafka
  • Built low-latency data lookup APIs
  • Done extensive performance optimizations
  • Built internal tooling foundations for performance testing, load testing and benchmarking
  • Bonus: Experience with time-series or columnar databases (e.g., ClickHouse, TimescaleDB, DuckDB)
  • Bonus: Familiarity with distributed query engines or streaming frameworks (Presto, Trino, Flink, Spark Streaming)
  • Bonus: Hands-on with container orchestration and scaling systems (Kubernetes, EKS)
  • Bonus: Experience building observability and performance tooling (Grafana, OpenTelemetry, flame graphs)