Overview:
The PSR SME owns the design and delivery of the Product & Service Record data model — the authoritative representation of how CUSTOMER's network products are structured and related to underlying infrastructure. The model underpins AI-driven event correlation, anomaly detection, root cause analysis, and service impact assessment across the AI Ops platform.
Key Responsibilities
PSR Model Design
-
Design end-to-end PSR data model for in-scope products (IP Connect, MPLS, SD-WAN).
-
Define PSR entities, attributes, relationships, and hierarchies.
-
Develop generic model first; iterate to CUSTOMER-specific as data is confirmed.
-
Validate model against current Catalog, Inventory and Ordering systems.
Source System Analysis
-
Analyse current Catalog, Inventory and Ordering systems for schemas, structures, and gaps.
-
Map data lineage from source systems to PSR model.
-
Raise structured data requests to CUSTOMER stakeholders.
Stakeholder & Workshop Engagement
-
Lead PSR discovery workshops with CUSTOMER product and provisioning teams.
-
Align with CMDB SME on CI-to-product mapping.
-
Present findings and model designs to programme leadership.
Use Case Enablement
-
Define data requirements per AI Ops use case across ingest, enrich, correlate, and present layers.
-
Support event correlation and service impact design.
Programme Delivery
-
Author workstream deliverables; maintain RAID log.
-
Contribute to JIRA stories and sprint planning.
Ensure delivery aligns to PI features and sprint goals.
Key Deliverables
1 Generic PSR Data Model
Product-agnostic entities, attributes & relationships.
2 CUSTOMER-Specific PSR Models
Per-product models (IP Connect, MPLS, SD-WAN) validated against CUSTOMER data.
3 PSR Attribute Specification Sheet
Attribute detail: type, source, transformation rule, mandatory flag.
4 Source System Analysis Report
Current Catalog, Inventory and Ordering systems — schemas, gaps, quality findings.
5 Data Lineage Map (PSR)
Source-to-PSR traceability for all key data attributes.
6 PSR–CMDB Integration Design
How PSR entities map to CMDB CI classes.
7 Use Case Data Requirements
Data needs per AI Ops use case at each delivery layer.
8 Gap Analysis & Recommendations
Current state vs. PSR model requirements with prioritised actions.
9 PSR Workstream RAID Log
Ongoing risks, assumptions, issues, and dependencies.
Skills & Experience
Domain Knowledge
-
Telecom product & service structures (MPLS, IP Connect, SD-WAN).
-
Product catalogue and service inventory data models in a telco OSS/BSS context.
-
Network provisioning and fulfilment systems (billing gateways, order management).
-
CMDB data models and CI class design — ServiceNow preferred.
-
AI Ops concepts: event correlation, anomaly detection, service impact, root cause analysis.
-
Service assurance and fault management in network operations.
Technical Skills
-
Data modelling — entity-relationship, conceptual, logical, physical.
-
SQL / database querying for schema analysis and validation.
-
Data mapping and transformation specification writing.
-
Ability to read and interpret complex, under documented database schemas.
-
JIRA — user story creation and sprint tracking.
-
TMF SID certification - desirable
-
ServiceNow (ITSM, CMDB, Event Management) — desirable.
-
Kafka / event streaming awareness — desirable.
Behavioural & Consulting
-
Stakeholder engagement — extracting requirements from time-poor CUSTOMER SMEs.
-
Structured problem-solving in data-poor, ambiguous environments.
-
Persistence in accessing and validating data from complex legacy systems.
-
Clear communication — presenting data models to technical and non-technical audiences.
-
Proactive risk identification and escalation.
Comfortable with iterative, agile delivery and progressive elaboration.
Responsibilities:
The PSR SME owns the design and delivery of the Product & Service Record data model — the authoritative representation of how CUSTOMER's network products are structured and related to underlying infrastructure. The model underpins AI-driven event correlation, anomaly detection, root cause analysis, and service impact assessment across the AI Ops platform.
Key Responsibilities
PSR Model Design
-
Design end-to-end PSR data model for in-scope products (IP Connect, MPLS, SD-WAN).
-
Define PSR entities, attributes, relationships, and hierarchies.
-
Develop generic model first; iterate to CUSTOMER-specific as data is confirmed.
-
Validate model against current Catalog, Inventory and Ordering systems.
Source System Analysis
-
Analyse current Catalog, Inventory and Ordering systems for schemas, structures, and gaps.
-
Map data lineage from source systems to PSR model.
-
Raise structured data requests to CUSTOMER stakeholders.
Stakeholder & Workshop Engagement
-
Lead PSR discovery workshops with CUSTOMER product and provisioning teams.
-
Align with CMDB SME on CI-to-product mapping.
-
Present findings and model designs to programme leadership.
Use Case Enablement
-
Define data requirements per AI Ops use case across ingest, enrich, correlate, and present layers.
-
Support event correlation and service impact design.
Programme Delivery
-
Author workstream deliverables; maintain RAID log.
-
Contribute to JIRA stories and sprint planning.
Ensure delivery aligns to PI features and sprint goals.
Key Deliverables
1 Generic PSR Data Model
Product-agnostic entities, attributes & relationships.
2 CUSTOMER-Specific PSR Models
Per-product models (IP Connect, MPLS, SD-WAN) validated against CUSTOMER data.
3 PSR Attribute Specification Sheet
Attribute detail: type, source, transformation rule, mandatory flag.
4 Source System Analysis Report
Current Catalog, Inventory and Ordering systems — schemas, gaps, quality findings.
5 Data Lineage Map (PSR)
Source-to-PSR traceability for all key data attributes.
6 PSR–CMDB Integration Design
How PSR entities map to CMDB CI classes.
7 Use Case Data Requirements
Data needs per AI Ops use case at each delivery layer.
8 Gap Analysis & Recommendations
Current state vs. PSR model requirements with prioritised actions.
9 PSR Workstream RAID Log
Ongoing risks, assumptions, issues, and dependencies.
Skills & Experience
Domain Knowledge
-
Telecom product & service structures (MPLS, IP Connect, SD-WAN).
-
Product catalogue and service inventory data models in a telco OSS/BSS context.
-
Network provisioning and fulfilment systems (billing gateways, order management).
-
CMDB data models and CI class design — ServiceNow preferred.
-
AI Ops concepts: event correlation, anomaly detection, service impact, root cause analysis.
-
Service assurance and fault management in network operations.
Technical Skills
-
Data modelling — entity-relationship, conceptual, logical, physical.
-
SQL / database querying for schema analysis and validation.
-
Data mapping and transformation specification writing.
-
Ability to read and interpret complex, under documented database schemas.
-
JIRA — user story creation and sprint tracking.
-
TMF SID certification - desirable
-
ServiceNow (ITSM, CMDB, Event Management) — desirable.
-
Kafka / event streaming awareness — desirable.
Behavioural & Consulting
-
Stakeholder engagement — extracting requirements from time-poor CUSTOMER SMEs.
-
Structured problem-solving in data-poor, ambiguous environments.
-
Persistence in accessing and validating data from complex legacy systems.
-
Clear communication — presenting data models to technical and non-technical audiences.
-
Proactive risk identification and escalation.
Comfortable with iterative, agile delivery and progressive elaboration.
Requirements:
The PSR SME owns the design and delivery of the Product & Service Record data model — the authoritative representation of how CUSTOMER's network products are structured and related to underlying infrastructure. The model underpins AI-driven event correlation, anomaly detection, root cause analysis, and service impact assessment across the AI Ops platform.
Key Responsibilities
PSR Model Design
-
Design end-to-end PSR data model for in-scope products (IP Connect, MPLS, SD-WAN).
-
Define PSR entities, attributes, relationships, and hierarchies.
-
Develop generic model first; iterate to CUSTOMER-specific as data is confirmed.
-
Validate model against current Catalog, Inventory and Ordering systems.
Source System Analysis
-
Analyse current Catalog, Inventory and Ordering systems for schemas, structures, and gaps.
-
Map data lineage from source systems to PSR model.
-
Raise structured data requests to CUSTOMER stakeholders.
Stakeholder & Workshop Engagement
-
Lead PSR discovery workshops with CUSTOMER product and provisioning teams.
-
Align with CMDB SME on CI-to-product mapping.
-
Present findings and model designs to programme leadership.
Use Case Enablement
-
Define data requirements per AI Ops use case across ingest, enrich, correlate, and present layers.
-
Support event correlation and service impact design.
Programme Delivery
-
Author workstream deliverables; maintain RAID log.
-
Contribute to JIRA stories and sprint planning.
Ensure delivery aligns to PI features and sprint goals.
Key Deliverables
1 Generic PSR Data Model
Product-agnostic entities, attributes & relationships.
2 CUSTOMER-Specific PSR Models
Per-product models (IP Connect, MPLS, SD-WAN) validated against CUSTOMER data.
3 PSR Attribute Specification Sheet
Attribute detail: type, source, transformation rule, mandatory flag.
4 Source System Analysis Report
Current Catalog, Inventory and Ordering systems — schemas, gaps, quality findings.
5 Data Lineage Map (PSR)
Source-to-PSR traceability for all key data attributes.
6 PSR–CMDB Integration Design
How PSR entities map to CMDB CI classes.
7 Use Case Data Requirements
Data needs per AI Ops use case at each delivery layer.
8 Gap Analysis & Recommendations
Current state vs. PSR model requirements with prioritised actions.
9 PSR Workstream RAID Log
Ongoing risks, assumptions, issues, and dependencies.
Skills & Experience
Domain Knowledge
-
Telecom product & service structures (MPLS, IP Connect, SD-WAN).
-
Product catalogue and service inventory data models in a telco OSS/BSS context.
-
Network provisioning and fulfilment systems (billing gateways, order management).
-
CMDB data models and CI class design — ServiceNow preferred.
-
AI Ops concepts: event correlation, anomaly detection, service impact, root cause analysis.
-
Service assurance and fault management in network operations.
Technical Skills
-
Data modelling — entity-relationship, conceptual, logical, physical.
-
SQL / database querying for schema analysis and validation.
-
Data mapping and transformation specification writing.
-
Ability to read and interpret complex, under documented database schemas.
-
JIRA — user story creation and sprint tracking.
-
TMF SID certification - desirable
-
ServiceNow (ITSM, CMDB, Event Management) — desirable.
-
Kafka / event streaming awareness — desirable.
Behavioural & Consulting
-
Stakeholder engagement — extracting requirements from time-poor CUSTOMER SMEs.
-
Structured problem-solving in data-poor, ambiguous environments.
-
Persistence in accessing and validating data from complex legacy systems.
-
Clear communication — presenting data models to technical and non-technical audiences.
-
Proactive risk identification and escalation.
Comfortable with iterative, agile delivery and progressive elaboration.
