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

Company Description

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.

Join us to put AI to work for people.

Job Description

About the role

Health Log Analytics (HLA) ingests, stores, and analyzes the logs that power ServiceNow's AIOps suite. We want it to be the data backbone teams trust to find problems early and fix them fast.

HLA is a data platform for operational logs at high volume, built for ServiceNow’s large enterprise customers. The work spans the full data path: collection and ingestion, routing and enrichment, durable storage and retention, and fast, cost-aware query. Our ingest builds on OpenTelemetry, and we have our own Rust-based log storage. As data volume, customer needs, and AI-driven use cases grow, we need the platform to get more reliable, more efficient, and more useful.

We're looking for a senior technical leader to help set where HLA goes next. You'll work side by side with engineering and product leadership on the roadmap. You'll bring a clear point of view and show it with a prototype, not just a slide.

You'll set the technical bar for experienced engineers. You'll do it by being a great teammate, not the loudest person in the room.

What you'll do

• Help shape the HLA roadmap with engineering, product, and leadership. Bring both vision and a reality check.
• Work directly with customers to make sure HLA keeps delivering value as their needs change. Turn their feedback into clear, actionable input for product and the roadmap.
• Set architecture direction across ingest, storage, and query, and across teams you don't own.
• Write down your point of view, then build a prototype that helps others see it and get behind it.
• Say no, with reasons. That includes AI ideas with no evidence behind them, and timelines or designs that don't fit today's constraints. Know when to push past those constraints, too.
• Lead deep design reviews that raise the bar and leave people feeling sharper, not smaller.
• Define the reliability and efficiency bar for the HLA data path: completeness, durability, latency, availability, tenant isolation, and unit cost.
• Lead the technical approach to hard transitions, such as storage migrations, schema changes, replay, and upgrades with little or no downtime.
• Set clear architectural boundaries and contracts for the teams building collection, ingest, storage, anomaly detection, query, and AIOps experiences.
• Show how we build with AI. Weigh ideas against customer value, data quality, reliability, and cost. Run small experiments to help us decide what to scale.
• Set the example on quality, on-call, and learning from incidents.
• Mentor senior engineers and tech leads. Help grow the next technical leaders.
• Work as a real partner to engineering and product leadership. Learn in the open.

Qualifications

To be successful in this role you have

• Experience in leveraging or critical thinking about how to integrate AI into work processes, decision making, or problem solving. This includes using AI powered tools, automated workflows, analyzing AI-driven insights, or reasoning about AI’s impact on how infrastructure is built and run.
• 12+ years building and operating production distributed systems, with substantial ownership of large-scale data or platform systems. Equivalent depth shown through sustained technical leadership counts too.
• Broad grounding across data ingestion, storage and query, and AI on top of data. Deep expertise in at least one.
• Experience with ingest tools like Kafka, Pulsar, or Kinesis, and with columnar stores, time-series databases, Elasticsearch/OpenSearch, or object-storage designs
• A record of changing the direction of teams you didn't own, through credibility and clear thinking
• Evidence-based judgment on AI. You know where it belongs and where it doesn’t, and you’ve built safeguards for correctness, safety, and cost. You’ve shipped with it, or you’re a strong proponent with a clear point of view.
• Good judgment on tradeoffs: 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. You prototype and review deeply, and you write code when it counts.
• Clear communication. You can explain a tricky design to a new hire or a VP and have both of them get it.

Nice to Have:

• A background in observability, log analytics, OpenTelemetry or AIOps
• Experience at…