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Salary: USD 171,900 - 300,800 / annual
Health Log Analytics (HLA) is ServiceNow's data backbone for operational logs at enterprise scale, powering the AIOps suite. The platform ingests, stores, and analyzes logs for large enterprise customers, spanning the full data path: collection and ingestion, routing and enrichment, durable storage and retention, and fast, cost-aware query. The ingest layer builds on OpenTelemetry, and the company has built a Rust-based log storage system. As data volume, customer needs, and AI-driven use cases grow, the platform must become more reliable, more efficient, and more useful.
You will serve as a senior technical leader helping set HLA's direction. Working side by side with engineering and product leadership, you'll shape the roadmap, bring both vision and reality checks, and demonstrate ideas through prototypes rather than slides. You'll set the technical bar for experienced engineers through great teamwork and clear thinking.
Key responsibilities include: shaping the HLA roadmap with engineering, product, and leadership; working directly with customers to understand evolving needs and translate feedback into actionable product input; setting architecture direction across ingest, storage, and query; writing down your point of view and building prototypes to help others understand and support it; saying no with clear reasoning when ideas lack evidence or timelines don't fit constraints; leading deep design reviews that raise the bar; defining reliability and efficiency standards for the data path (completeness, durability, latency, availability, tenant isolation, unit cost); leading technical approaches to hard transitions like storage migrations and schema changes with minimal downtime; setting clear architectural boundaries and contracts for teams building collection, ingest, storage, anomaly detection, query, and AIOps experiences; demonstrating how to build with AI by weighing ideas against customer value, data quality, reliability, and cost; running small experiments to inform scaling decisions; setting the example on quality, on-call practices, and incident learning; mentoring senior engineers and tech leads to grow the next generation of technical leaders; and working as a true partner to engineering and product leadership.
REQUIREMENTS:
- 12+ years building and operating production distributed systems, with substantial ownership of large-scale data or platform systems (equivalent depth through sustained technical leadership also counts)
- Experience leveraging or critically thinking about how to integrate AI into work processes, decision making, or problem solving (using AI-powered tools, automated workflows, analyzing AI-driven insights, or reasoning about AI's impact on infrastructure)
- Broad grounding across data ingestion, storage and query, and AI on top of data; deep expertise in at least one area
- Experience with ingest tools (Kafka, Pulsar, Kinesis) and with columnar stores, time-series databases, Elasticsearch/OpenSearch, or object-storage designs
- Record of changing the direction of teams you didn't own through credibility and clear thinking
- Evidence-based judgment on AI: knowing where it belongs and where it doesn't, having built safeguards for correctness, safety, and cost; shipped with it or strong proponent with 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; ability to prototype, review deeply, and write code when it counts
- Clear communication: ability to explain tricky designs to new hires or VPs with equal clarity
NICE TO HAVE:
- 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
