{"id":3055907,"url":"https://alion.io/job/servicenow-sr-staff-software-engineer-distributed-systems-data-ingestion-storage-analytics-remote","title":"Sr Staff Software Engineer (Distributed Systems | Data Ingestion | Storage | Analytics) - Remote","company":{"id":97,"name":"ServiceNow","domain":"servicenow.com","url":"https://alion.io/company/servicenow","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":80,"open_postings":38,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Diego, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":171900,"max":300800,"currency":"USD","period":"year","gross":null,"usd_annual":300800},"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AIOps","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Apache Kafka","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"OpenSearch","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Python","optional":false},{"name":"Rust","optional":false},{"name":"Scala","optional":false},{"name":"ServiceNow","optional":false},{"name":"Time Series","optional":false},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-10-22T00:52:46Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-25T02:55:04Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"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.\nJoin us to put AI to work for people.\nAbout the role\nHealth 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.\nHLA 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.\nWe'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.\nYou'll set the technical bar for experienced engineers. You'll do it by being a great teammate, not the loudest person in the room.\nWhat you'll do\nHelp shape the HLA roadmap with engineering, product, and leadership. Bring both vision and a reality check.\nWork 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.\nSet architecture direction across ingest, storage, and query, and across teams you don't own.\nWrite down your point of view, then build a prototype that helps others see it and get behind it.\nSay 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.\nLead deep design reviews that raise the bar and leave people feeling sharper, not smaller.\nDefine the reliability and efficiency bar for the HLA data path: completeness, durability, latency, availability, tenant isolation, and unit cost.\nLead the technical approach to hard transitions, such as storage migrations, schema changes, replay, and upgrades with little or no downtime.\nSet clear architectural boundaries and contracts for the teams building collection, ingest, storage, anomaly detection, query, and AIOps experiences.\nShow 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.\nSet the example on quality, on-call, and learning from incidents.\nMentor senior engineers and tech leads. Help grow the next technical leaders.\nWork as a real partner to engineering and product leadership. Learn in the open.\n To be successful in this role you have\nExperience 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.\n12+ 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.\nBroad grounding across data ingestion, storage and query, and AI on top of data. Deep expertise in at least one.\nExperience with ingest tools like Kafka, Pulsar, or Kinesis, and with columnar stores, time-series databases, Elasticsearch/OpenSearch, or object-storage designs\nA record of changing the direction of teams you didn't own, through credibility and clear thinking\nEvidence-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.\nGood judgment on tradeoffs: consistency, latency, cost, and operational burden\nComfort with Kubernetes and cloud infrastructure at scale\nFluency in at least one of Go, Java, Scala, Rust, or Python. You prototype and review deeply, and you write code when it counts.\nClear communication. You can explain a tricky design to a new hire or a VP and have both of them get it.\nNice to Have:\nA background in observability, log analytics, OpenTelemetry or AIOps\nExperience at an AI-native company, or building agents and LLM-based features\nExperience with multitenant SaaS and per-tenant isolation\nWho you are\nHumble, smart, and you care about people\nYou have experience working remotely and leading remote teams\nYou make the people around you better, and you share the work and the credit\nYou're comfortable when the problem isn't fully defined, and you help define it\nYou turn small, messy things into big, solid ones\nYou care about the people who run what you build\nWhy join us\nYou'll work on real scale problems with real customers. You'll have room to shape the architecture and the direction of a product that matters. And you'll join a team that values clear thinking, honest feedback, and shipping.\nFor positions in this location, we offer a base pay of $171,900 - $300,800, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.\n Work Personas\nWe approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.\nEqual Opportunity Employer\nServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.\nAccommodations\nWe strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact  for assistance.\nExport Control Regulations\nFor positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.\nFrom Fortune. ©2026 Fortune Media IP Limited. All rights reserved. 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