Sr Staff Software Engineer (Distributed Systems, Data Ingestion, Storage &;?
New
J
JobgetherHealth log analytics
Based in the United StatesFull-TimeStaff
SalaryBase salary: $171,900–$300,800 per year, depending on geographic location, qualifications, skills, competencies, and experience. Additional compensation: Equity where applicable and variable or incentive compensation, depending on the role and applicable compensation arrangements.
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Job Details
- Experience
- 12+ years building and operating production distributed systems
- Required Skills
- KafkaKubernetesGoDistributed Systems
Requirements
- Have 12+ years building and operating production distributed systems, including substantial ownership of large-scale data platforms or infrastructure; equivalent depth through sustained technical leadership is also valued.
- Bring strong understanding of data ingestion, storage, querying, and AI applications built on data, with deep expertise in at least one area.
- Have experience with ingestion platforms such as Kafka, Pulsar, or Kinesis.
- Be familiar with columnar databases, time-series databases, Elasticsearch/OpenSearch, and object storage.
- Have experience operating Kubernetes and cloud infrastructure at scale.
- Be proficient in at least one of Go, Java, Scala, Rust, or Python.
- Have experience using AI-powered tools, automated workflows, or AI-driven insights, or critically evaluating AI’s impact on infrastructure.
- Be able to evaluate AI solutions and apply safeguards for correctness, safety, reliability, and cost.
- Demonstrate the ability to influence technical direction across teams and balance consistency, latency, cost, reliability, and operational burden.
- Explain complex technical concepts clearly to audiences from early-career engineers to senior executives, and mentor colleagues.
- Be comfortable defining direction amid ambiguity and turning early-stage ideas into scalable systems.
- Have experience working effectively in remote environments and leading distributed teams.
Responsibilities
- Define the platform’s long-term technical strategy and roadmap with engineering and product leadership.
- Work with customers to understand requirements and translate feedback into product insights.
- Set technical direction for collection, ingestion, routing, enrichment, storage, retention, and querying.
- Document architectural proposals and build prototypes to demonstrate feasibility and clarify trade-offs.
- Set standards for data completeness, durability, latency, availability, tenant isolation, and unit cost.
- Lead storage migrations, schema evolution, data replay, and platform upgrades while protecting data integrity.
- Evaluate AI opportunities, run targeted experiments, and assess outcomes for broader investment.
- Lead design reviews and promote code quality, on-call practices, and incident response standards.
- Mentor senior engineers and technical leads across teams.
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