Location: Remote 

Position Summary: 

Our Scalability team as a Senior Scalability Engineer focused on observability platform development and engineering productivity. In this role, you will define, own, and build Judi Health's organization-wide observability strategy, tooling, and platform products. Beyond maintaining infrastructure, you'll architect and develop a custom observability platform that gives engineering teams powerful, fast, and cost-effective visibility into every layer of our infrastructure—from application logs and metrics to distributed traces. You'll build production-grade internal products using React/TypeScript frontends with Python and Rust backends, creating tools that fundamentally improve how engineers at Judi Health debug, monitor, and optimize their systems. Working closely with leadership and cross-functional teams, your work will be foundational to platform stability, performance optimization, and developer productivity across our rapidly growing healthcare platform. 

Position Responsibilities: 

In this role, you'll own the observability infrastructure that powers our engineering organization. You will:  

• Architect observability platform: Design, implement, and maintain the LGTM stack (Loki, Grafana, Tempo, Mimir/Prometheus) as the primary observability platform across all engineering teams, making architectural decisions that balance cost, performance, and developer experience.
• Build internal observability products: Design and develop production-grade internal platform products with React/TypeScript frontends and Python/Rust backends that provide engineers with powerful log search, metrics visualization, and trace analysis capabilities.
• Develop custom log indexing systems: Architect and build high-performance log indexing solutions using Rust that process logs and provide sub-second search across billions of log lines at a fraction of the cost.
• Integrate SQL analytics for logs: Design and implement solutions leveraging AWS Athena or similar SQL query engines (DuckDB, ClickHouse) for ad-hoc log analysis and historical queries, enabling engineers to run complex SQL queries over S3-based log data for deep investigations and trend analysis. 
• Create advanced query interfaces: Build sophisticated web interfaces that allow engineers to query logs, metrics, and traces with features like saved queries, query templates, correlation analysis, and pattern detection, supporting both full-text search and SQL-based analytics. 
• Balance cloud-native and open-source: Architect solutions that thoughtfully leverage both AWS-managed services (CloudWatch, Athena, Kinesis) and open-source tooling (LGTM stack, Quickwit) to optimize for cost, performance, and operational flexibility based on use case requirements. 
• Integrate AWS observability: Design seamless integration between AWS CloudWatch Logs/Metrics and our custom observability platform, providing unified visibility across managed and self-hosted infrastructure. 
• Build intelligent alerting: Develop smart dashboards, monitors, and alerting systems that reduce noise, detect anomalies, and help teams respond to incidents quickly. 
• Partner with engineering teams: Work directly with product teams to integrate observability into their services, establish logging and metrics standards, and instrument code effectively, serving as the observability subject matter expert. 
• Enable performance optimization: Provide the observability foundation that allows the Scalability team to identify performance bottlenecks, track optimization impact, and measure platform stability with data-driven insights. 
• Establish observability standards: Define and document comprehensive observability standards including structured logging patterns, metric naming conventions, trace instrumentation, dashboard design principles, and query best practices. 
• Drive platform adoption: Lead workshops, create documentation, and build self-service tooling that democratizes observability across engineering, making it easy for teams to adopt best practices. 
• Demonstrate technical leadership: Mentor engineers on observability practices, lead architecture reviews for instrumentation approaches, and represent the Scalability team in cross-functional planning. 
• Work in an Agile/Scrum environment to continually deliver value to stakeholders and clients. 
• Code of Conduct: Responsible for adherence to the Capital Rx Code of Conduct including reporting of noncompliance. 

Required Qualifications: 

• 10+ years of software engineering or infrastructure engineering experience with demonstrated progression into technical leadership roles. 
• Several years of experience leading technical initiatives, building platform products, or serving as a subject matter expert on observability infrastructure. 
• Strong experience with React/TypeScript for frontend development and Python (Flask/SQLAlchemy) for backend services. 
• LGTM stack expertise: Deep production experience with Loki, Grafana, Tempo, and Prometheus/Mimir for logs, metrics, and distributed tracing at scale. 
• AWS observability: Extensive experience with AWS CloudWatch Logs and Metrics, including custom metrics, log insights, dashboard creation, and integration patterns. 
• SQL analytics for logs: Production experience with SQL-based log analytics using AWS Athena, DuckDB, or similar query engines for analyzing structured and semi-structured data at scale. 
• Cloud-native and open-source balance: Demonstrated ability to architect solutions leveraging both managed cloud services and open-source tooling, understanding trade-offs between operational overhead, cost, flexibility, and vendor lock-in. 
• Search and indexing experience: Hands-on experience building or operating search systems using OpenSearch, Elasticsearch, Lucene, Tantivy, or similar search and analytics engines. 
• Performance-critical systems: Experience building high-performance systems that process large volumes of data efficiently (millions of log lines, high-cardinality metrics). 
• Systems thinking: Deep understanding of distributed systems, microservices architectures, and the complex observability challenges they present. 
• Data at scale: Proven track record handling high-volume structured and unstructured logging data, identifying patterns, and building efficient search/query solutions that perform well under load. 
• Product mindset: Ability to build internal platform products that engineers love to use, with attention to UX, performance, and reliability. 

Preferred Qualifications: 

• Rust development experience: Production experience with Rust for building high-performance data processing, indexing, or search systems. Strong interest in learning Rust is acceptable if combined with systems programming experience in C/C++/Go. 
• Infrastructure as code: Experience with Terraform for managing observability infrastructure and AWS resources. 
• Additional observability platforms: Experience architecting or operating Datadog, New Relic, Splunk, or other enterprise observability platforms. 
• Advanced query languages: Deep expertise with PromQL, LogQL, SQL optimization, and query optimization for high-cardinality data. 
• Columnar storage formats: Experience with Parquet, ORC, or other columnar storage formats for efficient log storage and analytics on S3. 
• Incident management: Experience designing incident response workflows, postmortem processes, and SLO/SLI frameworks that drive reliability improvements. 
• Cost optimization: Track record of reducing observability costs while maintaining or improving capabilities (e.g., CloudWatch → S3/custom indexing migration). 
• Data pipelines: Experience with streaming data pipelines, ETL processes, or real-time data processing. 
• Distributed tracing: Deep knowledge of OpenTelemetry, Jaeger, Zipkin, or distributed tracing architectures. 
• Git expertise and experience working in a mono repository. 
• Previous Pharmacy Benefits Manager (PBM) or healthcare technology experience. 
• Experience building developer tools or internal platforms that improve engineering productivity. 

This range represents the low and high end of the anticipated base salary range for the NY - based position. The actual base salary will depend on several factors such as: experience, knowledge, and skills, and if the location of the job changes. 

Nothing in this position description restricts management’s right to assign or reassign duties and responsibilities to this job at any time. 

Spire is making a fundamental shift in how it operates its constellation. We are moving from a model where trained operators watch dashboards and escalate to experts, to one where the system is fully autonomous, self-healing, and data intelligent. This team is building that future, and this role sits at the center of it. 

The old model relies on operators, escalations, manual processes, and institutional knowledge locked in the heads of a few experts. It does not scale and it does not belong in a modern space company. We know what we want to replace it with: SRE principles applied to a constellation, autonomous fault handling, AI-driven anomaly detection, low toil, high data quality, and continuous improvement baked into the system. 

You will be one of the engineers who makes that real. 

What You Will Work On  

Autonomous Operations: 

• Design and build fault detection, isolation, and automated recovery pipelines for satellites and ground stations
• Drive elimination of manual processes. If a human is doing it repeatedly, automate it.
• Build space situational awareness systems so the fleet always knows its own state
• Develop operational automation for mission execution at scale

Telemetry and Data Platform: 

• Architect and build a flexible data platform that ingests, stores, and surfaces telemetry from space and ground assets
• Build analytics and visualization tools that surface operational insight and not just raw data
• Apply ML and AI techniques to detect anomalies, predict failures, and optimize constellation performance
• Work directly with data at every layer from pipeline design to query

Reliability Engineering: 

• Define and instrument SLOs and reliability metrics for space and ground assets
• Build monitoring and alerting systems that know what matters and ruthlessly suppress noise
• Own configuration control and software rollout processes for on-orbit and ground assets

Mission Software: 

• Build real-time dashboards and operator tooling that make mission status immediately legible
• Collaborate directly with satellite operators and mission teams to turn operational pain into software solutions
• Design systems for automated mission execution including planning, scheduling, and commanding at scale

Basic Qualifications

• 5+ years of professional software engineering, SRE, or DevOps experience
• Strong Python skills across backend services, data pipelines, and automation. Not just scripts.
• Real experience applying data analysis to solve complex operational or technical problems, comfortable working directly with data at every layer from pipeline design to query
• Solid Linux fundamentals including bash scripting and basic system administration
• Experience with containerized development and production environments
• The communication skills to work across engineering, operations, and mission teams and translate real-world requirements into working software

Preferred Qualifications

• Experience with data backends such as Postgres, Redis, Elasticsearch, or S3
• Hands-on experience with Databricks or modern data Lakehouse tooling
• Experience building or operating monitoring and alerting systems such as Grafana, Prometheus, or Nagios
• Infrastructure as Code experience with tools such as Terraform or Ansible
• ML or AI applications in an operational or monitoring context
• Experience with Python data visualization libraries

Spire operates a hybrid work model, and this position will require you to work a minimum of three business days per week in the office.

Access to US export-controlled software and/or technology may be required for this role. If needed, Spire will arrange the necessary licenses—this is not something candidates need to have before applying. _#LI-MK1

The estimated compensation range is $160,000.00 - $185,000.00, commensurate on experience and technical skillset. 

We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.

We are strictly looking for direct, full-time W2 employees. We do not engage with third-party staffing agencies, C2C, or 1099 independent contractors for this role.
 
 In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.

How you will make an impact:

Our Data In Flight (DIF) team owns the core pipelines that ingest and route user and event data into Iterable, powering identity, segmentation, and real-time personalization. We are building a multi-tenant data platform that application teams can build on top of, with clear contracts, SLOs, and guardrails. Our product and our Elasticsearch clusters are multi-tenant, and we run Elasticsearch on Kubernetes using Elastic Cloud on Kubernetes (ECK).

We’re looking for a Staff Software Engineer with a track record of operating Elasticsearch-backed data platforms in multi-tenant SaaS. You’ll be a hands-on IC who both designs distributed systems and acts as a platform “product owner” for app teams: understanding their needs, shaping the platform roadmap, and defining stable interfaces between product and data infrastructure.

How you will make an impact:

• Own and evolve core Data In Flight services (ingestion, user and event APIs, identity, system events) with a focus on reliability, performance, and cost.
• Lead the Elasticsearch strategy for our multi-tenant live user and event data: index and mapping design, shard strategy, and safeguards across shared clusters.
• Design and enforce platform contracts (APIs, data models, SLAs, and usage limits) that let product teams move quickly while keeping the data platform safe and scalable.
• Work across other data and application teams on initiatives such as Elasticsearch upgrades, guardrails, resharding, and event retention.
• Provide senior technical leadership during incidents and reviews, turning operational learnings into durable platform improvements.
• Mentor engineers on distributed systems, data modeling, and pragmatic use of Elasticsearch.

What you'll bring:

You likely have many of the experiences below, but not necessarily all of them:

• Hands-on Elasticsearch expertise in production:
• Supporting customer-facing features or platforms backed by ES
• Designing schemas and queries for high-cardinality, multi-tenant workloads
• Operating large clusters (capacity planning, scaling, incident response)
• Multi-tenant platform experience:
• Building shared data services used by multiple product teams
• Implementing rate limits, quotas, and noisy-neighbor protections
• Thinking in terms of contracts and SLOs, not one-off integrations
• Strong distributed systems background:
• JVM-based services (Scala/Java) and event-driven or streaming architectures
• Comfort with modern cloud infrastructure (AWS or similar), messaging, and caches
• Staff-level impact and collaboration:
• Leading cross-team projects from design through rollout
• Translating technical decisions into clear business and product trade-offs
• Clear written communication (RFCs, design docs, post-incident reviews)

Nice To Have:

• Experience with Scala and the Play framework or similar JVM stacks.
• Prior work on core user data platforms (identity, profile stores, event pipelines, or CDP-like systems).
• Exposure to FinOps / infra cost optimization in data-heavy systems.
• Experience in MarTech / AdTech or other high-volume, low-latency data domains.

Perks & Benefits:

• Competitive salaries, meaningful equity, & 401(k) plan
• Medical, dental, vision, & life insurance
• Balance Days (additional paid holidays)
• Fertility & Adoption Assistance
• Paid Sabbatical
• Flexible PTO
• Monthly Employee Wellness allowance 
• Monthly Professional Development allowance 
• Pre-tax commuter benefits
• Complete laptop workstation

The US base salary range for this position at the start of employment is $156,00 - $242,000. Within this range, individual pay is determined by specific US work location, as well as additional factors, including job-related skills, experience, relevant education or training, and internal equity considerations.

Please note that the range listed above reflects only base salary. The total compensation package includes variable pay (where applicable), equity, plus a range of benefits, including medical, dental, vision, and financial. In addition, we offer perks such as generous stipends for health & fitness and learning & development, among others.