Senior Distributed Systems Engineer (Data Platform)
New
C
CensysInternet intelligence
For this role, we are open to remote employees across the continental US.Full-TimeSenior
SalaryFor high cost of living areas (San Francisco Bay, New York City, and Seattle), the expected salary range for this position is $174,000 USD - $206,000 USD, plus bonus eligibility and equity. For all other locations, the expected salary range for this position is $151,000 USD - $191,000 USD, plus bonus eligibility and equity.
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Job Details
- Experience
- 5+ years of software engineering experience building distributed systems
- Required Skills
- AWSArtificial IntelligenceGCPKafkaKubernetesAzureGoDistributed Systems
Requirements
- Have 5+ years of software engineering experience building distributed systems, such as data ingestion pipelines, databases, or services.
- Have experience with object-oriented programming; the team uses Go.
- Have experience with at least one cloud provider, such as AWS, Azure, or GCP.
- Have experience with or familiarity with message queue technologies such as AWS Kinesis, Google Pub/Sub, or Kafka.
- Have experience working with databases such as BigTable, Cloud Spanner, HBase, or Cassandra.
- Understand core distributed systems concepts, including scalability, fault tolerance, and reliability.
- Be familiar with using AI and open to applying it responsibly to improve efficiency and work.
- Be able to write understandable, testable, maintainable code.
- Communicate effectively and work with engineers and product managers.
- Familiarity with gRPC or REST is a plus.
- Familiarity with data serialization technologies such as Protobuf or MessagePack is a plus.
- Experience building, deploying, or maintaining containerized services in Kubernetes is a plus.
- Understanding how Internet-connected machines and services communicate using protocols and standards is a plus.
- Security domain knowledge is a bonus.
Responsibilities
- Build large-scale, real-time services and applications that use large datasets to power internal APIs and external applications.
- Build tooling, libraries, frameworks, and services supporting security, research, and data platform initiatives.
- Productionize prototypes into reliable internal tools, services, or platform capabilities.
- Participate in planning and technical discussions with engineering and product teams.
- Develop and maintain data pipelines, messaging systems, databases, caching layers, and services running in the cloud or on-premises.
- Work with Machine Learning Engineers and Security Researchers to develop solutions affecting security outcomes.
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