Sr Staff Software Engineer (Distributed Systems | Data Ingestion | Storage | Analytics) - Remote

ServiceNow•San Diego, CA

1d•$171,900 - $300,800•Remote

About The Position

ServiceNow is seeking a senior technical leader for Health Log Analytics (HLA), a high-volume operational log data platform that supports its AIOps suite and enterprise customers. HLA covers the full data path: collection and ingestion, routing and enrichment, durable storage and retention, and fast, cost-aware querying. Its ingestion is built on OpenTelemetry, and it uses Rust-based log storage. The role will help evolve the platform as data volumes, customer needs, and AI-driven use cases grow, with a focus on reliability, efficiency, and usefulness. The engineer will partner closely with engineering and product leadership to shape the HLA roadmap, set technical direction across ingest, storage, and query, and influence teams beyond their direct ownership. The role combines technical vision with hands-on prototyping, deep design reviews, customer engagement, architectural leadership, and mentorship. It also calls for establishing reliability and efficiency expectations across the data path, guiding difficult platform transitions, and assessing AI ideas through evidence, customer value, data quality, reliability, and cost. The position is remote as indicated in the job title, while the listed work persona is Flexible. The role offers a base pay range of $171,900–$300,800, plus equity when applicable, variable or incentive compensation, and benefits. Compensation varies by qualifications, skills, competencies, and work location.

Requirements

12 or more years building and operating production distributed systems, with substantial ownership of large-scale data or platform systems; equivalent depth demonstrated through sustained technical leadership also counts.
Broad grounding across data ingestion, storage and query, and AI on top of data, with deep expertise in at least one area.
Experience with ingestion tools such as Kafka, Pulsar, or Kinesis, and with columnar stores, time-series databases, Elasticsearch/OpenSearch, or object-storage designs.
A record of influencing the direction of teams not directly owned through credibility and clear thinking.
Evidence-based judgment about where AI is appropriate, with safeguards for correctness, safety, and cost; experience shipping AI or a strong, well-reasoned point of view.
Good judgment about tradeoffs involving consistency, latency, cost, and operational burden.
Comfort with Kubernetes and cloud infrastructure at scale.
Fluency in at least one of Go, Java, Scala, Rust, or Python, including the ability to prototype, review code deeply, and write code when needed.
Clear communication skills, including the ability to explain complex designs to audiences ranging from new hires to vice presidents.
Experience leveraging AI or critically considering how to integrate it into work processes, decision-making, or problem-solving, including AI-powered tools, automated workflows, analysis of AI-driven insights, or reasoning about AI’s impact on infrastructure.

Nice To Haves

Background in observability, log analytics, OpenTelemetry, or AIOps.
Experience at an AI-native company or building agents and LLM-based features.
Experience with multitenant SaaS and per-tenant isolation.
Experience working remotely and leading remote teams.

Responsibilities

Help shape the HLA roadmap with engineering, product, and leadership, balancing vision with practical constraints.
Work directly with customers, turn feedback into actionable product and roadmap input, and ensure HLA continues to deliver value as customer needs change.
Set architecture direction across ingestion, storage, and query, including influencing teams outside the role’s direct ownership.
Document technical viewpoints and build prototypes to demonstrate and validate proposed directions.
Evaluate proposals and timelines against evidence and current constraints, including AI ideas, and identify when constraints should be challenged.
Lead in-depth design reviews that improve technical quality and help engineers grow.
Define reliability and efficiency expectations for the HLA data path, including completeness, durability, latency, availability, tenant isolation, and unit cost.
Lead technical approaches to difficult transitions such as storage migrations, schema changes, replay, and upgrades with little or no downtime.
Set architectural boundaries and contracts for teams building collection, ingestion, storage, anomaly detection, query, and AIOps experiences.
Evaluate AI opportunities against customer value, data quality, reliability, and cost, and run small experiments to inform decisions about scaling.
Set an example in software quality, on-call practices, and learning from incidents.
Mentor senior engineers and tech leads and help develop future technical leaders.
Partner with engineering and product leadership and foster open learning.