Senior Staff Engineer - Network Telemetry & Data Platforms
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Equinix is the world's digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.
A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future.
Help us challenge assumptions, uncover bias, and remove barriers-because progress starts with fresh ideas. You'll find belonging, purpose, and a team that welcomes you-because when you feel valued, you're empowered to do your best work.
Job Summary
We are looking for a hands-on Sr. Staff Engineer to own the architecture and delivery of Network Observability's data and ETL layer. You will build the pipelines that collect network events, logs, and metrics at scale and turn them into correlated, enriched, decision-ready data that operators, customers, APIs, and AI agents can trust. You will be the feature lead for large, ambiguous problems. You will break them into use-case-driven increments that deliver value early, and you will write code, set technical direction, and raise the engineering bar for the team.
Responsibilities
Feature Leadership
- Turn large, loosely defined asks (e.g., "unified network health," "proactive anomaly detection") into use cases, data contracts, and phased delivery plans with clear MVPs and success metrics
- Partner with product, NOC, network engineering, and API teams to prioritize by operational impact, and decide when to build, buy, or adopt open source
- Write design docs, drive architecture reviews, and lead by example in code and on-call
Telemetry Ingestion & Processing
- Build collection pipelines for SNMP, gNMI/streaming telemetry, syslog, and flow data on messaging platforms such as Kafka, NATS, RabbitMQ, or Pulsar
- Define schemas, serialization formats, and data contracts across producers and consumers
- Build real-time processing in Apache Flink (or equivalent) for normalization, enrichment, deduplication, and correlation, and batch/backfill pipelines in Apache Spark
- Design for well-understood delivery semantics, late and out-of-order data, replay, and data quality checks
Insight, Correlation & Storage
- Turn raw events, logs, and metrics into decision-data, correlated across devices, links, and services and enriched with topology and inventory context
- Select and design fit-for-purpose storage: ClickHouse or similar OLAP stores, Druid, Prometheus with Thanos (or similar), and ELK for logs and events
- Own data modeling, retention, downsampling, and cost and performance trade-offs at scale
AI-Native Development
- Partner with the Network API and AI teams so MCP servers and agents get governed, high-quality, well-documented data surfaces, with freshness and reliability included
- Use AI-assisted development tools in daily work and help set team practices for reviewing AI-generated code
- Support building data tools for Observability MCP
Reliability & Technical Leadership
- Define SLOs for pipeline freshness, completeness, and accuracy, and monitor the pipelines themselves
- Lead RCA for complex issues such as data gaps, lag, cardinality explosions, and schema drift
- Mentor engineers, review designs and code, and influence the multi-year data roadmap
Qualifications
Required
- 7+ years of software engineering experience, with significant time building large-scale data pipelines, streaming platforms, or telemetry systems
- Deep, hands-on experience with at least one major streaming platform (Kafka, Pulsar, NATS, RabbitMQ)
- Production experience with stream processing (Flink or similar) and batch processing (Spark)
- Strong understanding of network observability data (events, logs, metrics) and protocols (SNMP, gNMI, syslog, flow data)
- Experience storing and querying telemetry at scale in ClickHouse, Druid, or similar, and in Prometheus/Thanos or similar timeseries systems
- Working experience with ELK/Elastic (or OpenSearch)
- Strong programming skills in Java/Springboot, or Python
- Daily use of AI coding assistants, and familiarity with MCP or structured tool interfaces for AI agents
- Proven ability to break down ambiguous problems into incremental deliveries and lead features end to end
- Strong written and verbal communication
- Bachelor's degree in Computer Science, Engineering, or equivalent experience
Preferred
- Experience in network EMS/NMS, telecom, data center, or interconnection environments (e.g., Equinix Fabric)
- Knowledge of network concepts (BGP, OSPF, MPLS) and inventory/topology systems (CMDB, DCIM, graph models)
- Experience with OpenTelemetry, Grafana, Loki, and Kubernetes-based data platforms
- Experience building data products for ML/AI use cases, or building and operating MCP servers
- Open source contributions in streaming, observability, or data infrastructure
What You'll Own
- The architecture and roadmap of Network Observability's ingestion, processing, and storage layers
- Correlated, enriched, trustworthy network data that powers APIs, dashboards, and AI agents
- A use-case-driven delivery model that ships value in increments
Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.
Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.
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This posting is a new position within our organization.