Architect, Service & Operational Data

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** This is a hands-on architecture role with strong cross-functional leadership: you'll partner with SRE, IT Ops, Security, Engineering, and Analytics to define the operational data strategy and deliver measurable improvements in availability, customer experience, MTTR, change risk, and operational efficiency.

Key Responsibilities
** About the Role
** As an
** Architect, Service & Operational Data**, you will:
* Define the operational data strategy and target architecture (current-to-future state), including domains such as incidents, changes, problems, requests, alerts/events, assets/CMDB, SLOs/SLIs, capacity, and cost.
* Design data models and canonical schemas for service and operational datasets (e.g., service topology, event/alert normalization, incident timelines, change-to-incident linkage).
* Build and govern reliable pipelines that ingest and transform data from tools like ITSM, observability platforms, cloud logs/metrics/traces, CI/CD, and infrastructure systems.
* Establish data quality and trust: data contracts, lineage, validation rules, SLAs/SLOs for datasets, and operational monitoring for pipelines.
* Create "operational data products" (curated datasets, semantic layers, metrics definitions) that enable dashboards, analytics, and automation (AIOps, correlation, anomaly detection).
* Partner with stakeholders (SRE/IT Ops/Service Owners) to identify high-value use cases: incident reduction, alert noise reduction, change failure rate, capacity planning, and reliability insights.
* Implement governance and security controls for sensitive operational data (RBAC/ABAC, retention, encryption, auditability, and policy-based access).
* Set standards and reference implementations for tooling, integration patterns, and best practices across teams.
* Mentor engineers/analysts and influence roadmap decisions through architecture reviews and technical leadership.
** About you
** You are a potential fit for the role
** Architect, Service & Operational Data**, if your background includes:

Required Qualifications
* 8+ years in data architecture, platform/analytics engineering, or operational analytics (with 3+ years in a lead/architect role).
* Proven experience designing enterprise data architectures (batch + streaming) and delivering scalable data platforms.
* Strong understanding of service operations and reliability concepts: ITIL/ITSM, incident/change/problem workflows, SLOs/SLIs, event management, and operational KPIs (e.g., MTTR, CFR).
* Hands-on expertise with modern data stack components:
Data modeling (dimensional, Data Vault, domain/event-driven patterns), ETL/ELT and orchestration, Data quality/testing frameworks, Metadata, lineage, and cataloging, Strong SQL and at least one programming language (Python strongly preferred),

Experience with cloud data ecosystems (AWS/Azure/GCP) and security fundamentals, and Excellent communication skills and ability to align diverse teams around standards and measurable outcomes.

Preferred / Nice-to-Have
* Experience integrating operational tooling such as Service Now, Jira Service Management, Pager Duty/Opsgenie, Dynatrace/New Relic/Datadog, Splunk/Elastic, Prometheus/Grafana, Open Telemetry.
* Streaming/event technologies (e.g., Kafka/Kinesis/Pub Sub) and real-time analytics patterns.
* Knowledge graph / topology modeling for service dependency mapping.
* Experience enabling AIOps use cases: correlation, deduplication, anomaly detection, incident prediction.
* Familiarity with Fin Ops or capacity/cost analytics tied to service health.
* Certifications (cloud, data, ITIL) are a plus.
* What Success Looks Like (First 6–12 Months)
* A documented target architecture and prioritized roadmap for service & operational data.
* A canonical operational data model adopted across key domains (incidents, changes, alerts, services/topology).
* Measurable improvements in data reliability (freshness, completeness, accuracy) and stakeholder trust.
* Delivered high-impact use cases (e.g., reduced alert noise, improved MTTR reporting accuracy, change risk insights).
* Why This Role Matters
* Operational data is often the most valuable—and most…