Senior Staff Engineer - Network Telemetry & Data Platforms

Overview

Senior Staff Engineer to own the architecture and delivery of Network Observability's data and ETL layer, building pipelines that collect network events, logs, and metrics at scale and turn them into correlated, enriched, decision-ready data.

What you'll do

• 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.
• 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.
• 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.
• 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.
• 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.

What you'll need

• 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.

Nice to have

• 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.

Details

• Location: Bangalore Office BLS2.
• New position within the organization.