Engineering

Senior Backend Platform Engineer at Spire

San Francisco, CA   |   Full time


Every day, Spire measures 30 billion data-points of real-time core physiological signals from our users: breathing, heart rate, emotional states and stress, sleep, all cross-referenced by demographic data. We have the largest existent data set biometric signal correlated with state of mind (SOM) that has ever been captured.


You will build the infrastructure that turns that data into meaningful insights that change people's lives.


This means: building our big-data pipeline on AWS. Building it, instrumenting it, optimizing it, and most importantly ensuring it's secure. You will integrate different data-stores, latency requirements, algorithms and security needs into one well oiled big-data machine (on AWS).


Success will be measured in your ability to implement, execute on, and maintain core company initiatives ranging from security and privacy to scalability and uptime, all with an eye toward optimized performance.


What you’ll be doing

  • Architect backend systems to deliver real-time insights seamlessly to external API and enterprise/consumer end users
  • Design and implement data pipelines capable of storing 100s of billions of physiological data points per day and making it available via API with ultra low latency
  • Develop software automation and tooling to drive efficiencies and scale our core infrastructure
  • You Dream of serverless architecture and APIs - develop and expand our collection of single-purpose lambda functions.
  • Monitor, measure, and optimize the performance, effectiveness, and uptime of our backend services
  • Define and develop deployable architecture through code using Terraform on AWS

What you’ll work with


Our stack; Python, Ruby, and Scala hosted on AWS and running inside lambda functions or in Docker. An excellent candidate will demonstrate knowledge in at least some of our tools and demonstrate an ability and willingness to both teach and learn from others.


A few of the services/tools/libraries we use

Languages: Python, Ruby, Scala, Typescript
APIs: GraphQL, REST, and Protobuf
Datastores: PostgreSQL, DynamoDB, Redshift, Memcache, S3
Infrastructure: Terraform, Cloudflare (DNS/CDN), ELB/ALB, Kinesis
ML & Compute: Tensorflow, Flink, Spark, Pandas/Numpy
Logging: Cloudwatch, Elasticache, Logstash, Kibana (ELK)
Analytics: Segment/Looker

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