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

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Sr Staff Software Engineer (Distributed Systems | Data Ingestion | Storage | Analytics) based in the United States.
We are seeking a senior technical leader to shape the future of a large-scale health log analytics platform that powers intelligent operations and helps enterprise customers identify and resolve issues faster. In this role, you will influence the technical roadmap, guide architecture across data ingestion, storage, and query systems, and drive improvements in reliability, performance, and cost efficiency. You will collaborate closely with engineering, product, and customer-facing teams to turn complex challenges into scalable solutions. Through hands-on prototyping, thoughtful technical reviews, and evidence-based decision-making, you will help establish architectural direction across multiple teams. You will also champion practical, responsible AI adoption while mentoring senior engineers and developing future technical leaders. This is an opportunity to make a lasting impact on a mission-critical data platform in a collaborative, innovation-driven environment.
Accountabilities
Shape technical strategy and roadmap: Partner with engineering and product leadership to define the platform's long-term direction, balancing architectural vision with practical constraints and customer needs.
Drive customer-centered innovation: Work directly with customers to understand evolving requirements, translate feedback into actionable product insights, and ensure the platform continues to deliver measurable value.
Lead architecture across the data lifecycle: Establish technical direction for collection, ingestion, routing, enrichment, durable storage, retention, and high-performance querying across distributed systems and multiple engineering teams.
Prototype and validate solutions: Document architectural proposals and build working prototypes that demonstrate feasibility, clarify trade-offs, and help teams align around technical decisions.
Establish reliability and efficiency standards: Define and improve expectations for data completeness, durability, latency, availability, tenant isolation, and unit cost across the entire data pipeline.
Guide complex technical transitions: Lead approaches to storage migrations, schema evolution, data replay, and platform upgrades while minimizing downtime and protecting data integrity.
Set architectural boundaries and contracts: Define clear interfaces and responsibilities across teams working on collection, ingestion, storage, anomaly detection, querying, and AIOps experiences.
Champion responsible AI adoption: Evaluate AI-driven opportunities based on customer value, data quality, reliability, safety, and cost. Run targeted experiments, validate outcomes, and identify which solutions merit broader investment.
Raise engineering quality: Lead in-depth design reviews that strengthen technical decisions, encourage constructive discussion, and help engineers develop their capabilities.
Promote operational excellence: Establish strong standards for code quality, on-call practices, incident response, and continuous learning from production issues.
Mentor technical leaders: Coach senior engineers and technical leads, foster collaboration, and help develop the next generation of engineering leadership.
Influence across teams: Build trust through clear communication, sound technical judgment, and collaborative leadership, including with teams outside your direct ownership.
Requirements
Extensive distributed systems experience: 12+ years building and operating production distributed systems, including substantial ownership of large-scale data platforms or infrastructure. Equivalent depth demonstrated through sustained technical leadership is also valued.
Broad data platform expertise: Strong understanding of data ingestion, storage, querying, and AI applications built on data, with deep expertise in at least one of these areas.
Relevant data technologies: Experience with ingestion platforms such as Kafka, Pulsar, or Kinesis, and familiarity with technologies or architectures such as columnar databases, time-series databases, Elasticsearch/OpenSearch, and object storage.
Cloud-native infrastructure knowledge: Experience operating Kubernetes and cloud infrastructure at scale, with an understanding of distributed systems reliability and operational complexity.
Strong programming skills: Proficiency in at least one of Go, Java, Scala, Rust, or Python, with the ability to build prototypes, conduct detailed code reviews, and contribute directly to implementation when needed.
Evidence-based AI judgment: Experience using AI-powered tools, automated workflows, or AI-driven insights in work processes and decision-making, or critically evaluating AI's impact on infrastructure. Demonstrated experience shipping AI-enabled solutions—or a well-developed, practical perspective on their application—is important.
Responsible AI practices: Ability to evaluate AI solutions critically and implement appropriate safeguards for correctness, safety, reliability, and cost.
Architectural and technical leadership: A demonstrated ability to influence technical direction across teams without relying on formal authority, supported by clear reasoning, credibility, and sound judgment.
Trade-off management: Strong decision-making skills when balancing consistency, latency, cost, reliability, and operational burden.
Communication and collaboration: Ability to explain complex technical concepts clearly to audiences ranging from early-career engineers to senior executives, while fostering constructive debate and shared ownership.
People development: A humble, collaborative approach to leadership, with a commitment to mentoring others, sharing credit, and helping colleagues succeed.
Adaptability and ownership: Comfort working with ambiguous problems, defining direction where requirements are incomplete, and turning early-stage ideas into robust, scalable systems.
Remote leadership experience: Experience working effectively in remote environments and leading distributed teams.
Nice to have: Background in observability, log analytics, OpenTelemetry, or AIOps; experience at an AI-native organization or building agents and LLM-based features; and familiarity with multitenant SaaS platforms and per-tenant isolation.
Benefits
Base 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.
Health coverage: Health plans and flexible spending accounts.
Retirement savings: 401(k) plan with company matching.
Employee stock purchase: Employee Stock Purchase Plan (ESPP).
Giving back: Matching donations to support eligible charitable contributions.
Time away: Flexible time-away arrangements.
Family support: Family leave programs.
Flexible work environment: Work arrangements may vary by role and assigned location, with flexible, remote, or office-based work personas where applicable.
Technical impact and growth: Opportunities to solve complex, large-scale engineering challenges, shape platform architecture, work closely with enterprise customers, and help influence product direction.
Collaborative culture: An environment that values clear thinking, honest feedback, continuous learning, and delivering meaningful results.
How Jobgether Works
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Work arrangement
Yes