Integration Observability Architect

Role: Integration Observability Architect Locations: Remote (Dallas, Tx) Duration: 12 Months 
Skill Set
Enterprise Observability Architecture, Open Telemetry framework design, APM & Cloud monitoring platforms expertise, ITSM integration & event correlation, AIOps & anomaly detection, Kubernetes & microservices monitoring, Alert optimization & noise reduction, SLI/SLO framework definition, Integration architecture & governance standards
 
Job Description Experience & Purpose:
Minimum 15+ years of experience in the data domain, with strong expertise in defining and implementing monitoring and observability frameworks for enterprise-scale data ecosystems. Responsible for establishing a scalable data observability strategy across pipelines, batch workloads, databases, and integration layers to ensure end-to-end visibility, reliability, operational resilience, and business-impact awareness.
 Key Responsibilities

• Assess observability across:

• Batch jobs, schedulers, ETL/ELT pipelines, and data platforms

• Database monitoring, performance, and query behavior

• Integration and middleware workflows across systems

• Evaluation:

• Pipeline visibility (latency, failures, throughput, dependencies, data SLAs)

• Effectiveness of schedulers/orchestration platforms (e.g., ActiveBatch, Airflow, Control-M)

• Database observability and performance monitoring practices

• Identify:

• Blind spots in data flow, lineage, and cross-system dependencies

• Failure detection gaps beyond job-level (data quality, freshness, volume anomalies)

• Inefficiencies in retry mechanisms, alerting, and operational workflows

• Define:

• Standard observability patterns and frameworks for data workloads

• Dependency-aware monitoring models across upstream and downstream systems

• Actionable dashboards, alerts, and SLAs aligned to business impact

• Repeatable onboarding patterns for new pipelines and data services

• Enable intelligent observability:

• Reduce alert noise and improve signal quality and actionability

• Correlate events across pipelines, databases, and integrations

• Link technical failures to business outcomes and downstream impact

• Incorporate AI capabilities:

• Anomaly detection in pipeline behavior, data patterns, and performance trends

• Failure prediction and early warning signals for batch/data workflows

• Intelligent alerting and correlation across data ecosystems leveraging AIOps platforms such SNOW ITOM, Moog soft or Big Panda

• Contribute to:

• Target-state data observability architecture and engineering blueprint

• Retrofit and modernization guidance for existing pipelines and platforms