Senior Manager, Software Engineering (Observability, Kafka, Monitoring)

The Experience
You will be a Senior Manager of Software Engineering leading a team of 8 to 10 engineers. Your team builds the “Anomaly Alerting” platform. This is a smart system that watches for strange changes in data and sends out warnings. Your job is to guide the people, own the system, and help make our warning alerts smarter and more automatic.
What You’ll Actually Be Doing
Day-to-day work: You will make sure our alerting system is fast, strong, and does not break as it grows. You will lead projects that make the system do the hard work so other teams don’t have to set up alerts by hand.
Leading the way: You will manage 1 to 2 teams (8 to 10 engineers). You will build a happy, helpful culture where people work well together.
Working together: You will partner with other tech teams and groups across Salesforce (like Slack and Sales Cloud) so they can find and fix problems much faster. You will also speak for your team in meetings and work closely with our teams in the US.
Early goals & impact: In your first 60 to 90 days, you will learn the ropes, start leading in meetings, team up with the US groups, and make a real difference on our biggest goals.
You’re Our Person If...
You have experience being a manager and leading 8 to 10 software engineers.
You know how to handle huge amounts of data and build data pipelines (ETL).
You have experience running systems on big clouds like AWS or Google Cloud (GCP).
You have worked with tools that monitor system health (Observability).
You have some experience with Machine Learning and know how to spot strange patterns in data (Anomaly Detection).
You are able to work in the office as the company requires (no travel is required for this role).
Even Better If...
You have deep, hands-on experience building Machine Learning tools that send out alerts.
You already know how to work with Salesforce products like Agentforce, Commerce Cloud, or Slack.
Tech Stack:
Cloud & Compute: AWS, GCP, and Kubernetes (EKS/GKE) for stateless, scalable multi-cloud orchestration.
Infrastructure as Code: Terraform for resilient, automated platform provisioning across environments.
Edge Ingestion: OpenTelemetry (OTel) for standardized instrumentation, and Vector/Fluent Bit for high-throughput log routing.
Message Buffering: Apache Kafka to ingest billions of telemetry events, ensuring durable, decoupled data pipelines.
Stream Processing: Apache Flink for real-time log parsing, rolling aggregations, and anomaly detection before storage.
Backend Languages: Java for building heavy stream-processing applications, supplemented by Go and Python for operators and tooling.
Ad-Hoc OLAP Analytics: Apache Druid to power sub-second, interactive dashboard queries over massive historical datasets.
Log & Trace Storage: ClickHouse for fast columnar event storage, natively tiered to AWS S3 / GCP Cloud Storage for cost-effective cold storage.
Time-Series Metrics: VictoriaMetrics, M3DB, or Prometheus to ingest and compress millions of high-cardinality metric rollups.
Visualization & Alerting: Grafana and Apache Superset for single-pane-of-glass UI, paired with Prometheus Alertmanager for incident routing.
A demonstrated, genuine AI-first approach to engineering — using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows.
Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.

Skills
Kafka, Python, Java, Git, AWS, GCP, Kubernetes

Experience
5+ yrs

Employment type
Full time