Job Requirement
1. AWS & Cloud Fundamentals (Must-have)
- The engineer must understand how OpenSearch fits into the AWS ecosystem.
Core skills
- AWS core services:
- EC2, VPC, IAM
- S3 (log storage, snapshots)
- CloudWatch (logs, metrics, alarms)
- CloudTrail (API calls)
- AWS OpenSearch Service:
- Domain setup & scaling
- Shards, replicas, instance types
- Multi-AZ & high availability
- Security:
- IAM roles & policies
- VPC access, security groups
- Encryption at rest & in transit
- Has worked on Guard Duty, Security Hub, Security Lake
Why it matters
- Correlation programs ingest data from many AWS servicesmisconfigured access or scaling breaks everything.
2. OpenSearch / Elasticsearch Technical Skills (Critical)
- This is the core technical competency.
Search and indexing
- Index design & lifecycle management (ISM / ILM)
- Mappings (keyword vs text, nested fields)
- Sharding strategies for high-volume logs
- Index templates
Query and correlation
- OpenSearch Query DSL
- Aggregations (terms, date histograms, filters)
- Cross-index correlation (e.g., trace_id, session_id)
- Time-based analysis
Performance tuning
- Query optimization
- Hotwarm architectures
- Memory, heap sizing, JVM basics
3. Log, Event and Metric Correlation Skills (Core to the Program)
- This is what differentiates a correlation engineer from a normal OpenSearch admin.
Correlation concepts
- Event normalization & enrichment
Common identifiers:
- trace_id, request_id, transaction_id
- user_id, IP address, device_id
- Temporal correlation (events across time windows)
Multi-source correlation:
- Application logs
- Infrastructure logs
- Security events
- Business events
Examples
- Correlating API latency spikes with EC2 CPU and ALB logs
- Linking failed login attempts across apps and IAM logs
- End-to-end transaction tracing
4. Data Ingestion and Pipeline Skills (Very Important)
- Correlation depends on clean, structured data.
Ingestion tools
- OpenSearch Ingestion (OSI)
- Logstash
- Fluent Bit / Fluentd
- Kinesis Data Streams / Firehose
- AWS Lambda (custom processors)
Data processing
- Parsing JSON, CSV, unstructured logs
- Grok patterns
- Field enrichment (geo-IP, user agent)
- Timestamp normalization
5. Observability and SIEM Knowledge (Strong Advantage)
- Most correlation programs fall into one of these domains.
Observability
- Distributed tracing concepts
- Metrics vs logs vs traces
- APM integration (OpenTelemetry, X-Ray)
- Dashboards for SRE & Ops teams
Security / SIEM
- Security log types:
- VPC Flow Logs
- CloudTrail
- WAF logs
- Threat detection & alerting
- Correlation rules (multi-event detection)
- False-positive reduction
6. Analytics, Dashboards and Alerting
- Correlation must be consumable by humans.
Skills
- OpenSearch Dashboards:
- Visualizations
- Lens / TSVB-like analytics
- Custom dashboards for ops & security
- Alerting:
- Threshold-based alerts
- Anomaly detection
- Event-based alerts
- Reporting for incidents & audits
7. Automation and DevOps Skills
- Large-scale correlation programs cannot be managed manually.
Required skills
- Infrastructure as Code:
- Terraform / CloudFormation
- CI/CD for:
- Index templates
- Dashboards
- Alert rules
- Scripting:
- Python (log processing, APIs)
- Bash
- API usage:
- OpenSearch REST APIs
8. Non-Technical but Critical Skills
- Often underestimated, but essential for success.
- Requirement analysis (what to correlate & why)
- Stakeholder communication (Ops, Security, App teams)
- Incident response collaboration
- Documentation & runbooks
- Data governance & retention compliance (important in regulated industries)
