Senior Redis Engineer

You'll master advanced commands (`SCAN`, `BITFIELD`, `EVAL`), debug performance using `MONITOR`, `SLOWLOG`, `LATENCY HISTOGRAM`, and generally know Redis internals inside out. You're the go-to person for deep-dive diagnostics.

You'll be provisioning, configuring, and scaling Redis clusters on our chosen cloud platform. This means managing IAM roles, network policies, and making sure everything is secure and optimised for cost and performance.

You'll manage Redis clusters using a Kubernetes Operator (e.g., Redis Enterprise Operator, Bitnami). This involves configuring StatefulSets, Persistent Volumes, and network policies to ensure resilient, scalable deployments in our containerised environment.

You'll build custom dashboards and complex alerts using PromQL or similar query languages. You'll trace performance issues across multiple services, ensuring we have full visibility into our Redis infrastructure's health.

You'll write complex, reusable Terraform modules and Ansible playbooks from scratch to automate Redis deployments, upgrades, and operational tasks. You'll also automate failover testing and backup/restore procedures.

You'll optimise application-level interaction with Redis, implementing connection pooling, client-side caching, and robust error handling. You'll benchmark library performance and advise application teams on best practices.

• Application Development Teams (they're your primary 'customers')
• Site Reliability Engineering (SRE) Team (you'll work closely on uptime and incident response)
• Product Owners (they'll tell you what features need fast data access)
• Data Platform Team (for broader data strategy and integration)
• Security Team (to make sure everything is locked down tight)

• Cloud Providers (AWS, GCP, Azure for managed Redis services)
• Redis Enterprise (if we use their commercial offerings)
• Tooling Vendors (for monitoring, IaC, etc.)

Redis is more than just a key-value store now. Modules like RediSearch, RedisJSON, RedisGraph, and TimeSeries are becoming critical for specific use cases (e.g., real-time search, document stores, graph analytics). You'll need to know when and how to deploy and optimise these.

RediSearch Indexing & Querying · RedisJSON Data Manipulation · RedisGraph Cypher Queries · Module Performance & Scalability

• This week: Pick one module (e.g., RediSearch) and read its official documentation thoroughly.
• This month: Implement a small project using that module, perhaps replacing an existing search function.
• Month 2: Evaluate another module (e.g., RedisJSON) for a potential use case within our existing applications.
• Month 3: Present findings on the benefits and challenges of using Redis Modules to the team.

Quick win: Experiment with RediSearch to create a simple autocomplete service for an internal tool. It's a quick win that shows off the power of modules.

As our Redis footprint grows, manual monitoring and tuning become unsustainable. AI/ML will increasingly be used to predict issues, suggest optimisations, and even automate remediation, moving us towards truly self-healing systems.

Anomaly Detection Algorithms · Predictive Capacity Planning · Reinforcement Learning for Tuning · Observability Data Integration

• This week: Research existing AIOps platforms that integrate with Redis (e.g., Datadog's anomaly detection).
• This month: Set up a custom anomaly detection alert in our monitoring system for a critical Redis metric.
• Month 2: Experiment with a simple Python script to forecast Redis memory usage using a basic time-series model.
• Month 3: Propose how we could integrate AI-driven insights into our Redis operational dashboards.

Quick win: Configure a simple 'outlier detection' alert on Redis P99 latency in your existing monitoring system. It's a small step towards AI-driven ops.