Description

Key Responsibilities
Own current-state assessment, target-state architecture, transition roadmap and architecture decision records. Design the end-to-end data platform across ingestion, lake, Synapse, curated, semantic, Al and consumption layers. Define integration patterns for batch, CDC, API, files, events and external partner data.
Guide canonical modeling, data product boundaries, metadata, lineage, quality and governance architecture. Define AI-ready data patterns and interfaces for model development, deployment, monitoring and responsible usage. Establish security, identity, network, encryption, privacy, retention, resilience and disaster recovery principles. Review designs and code approaches for scalability, performance, cost, operability and standards compliance. Lead client architecture workshops, trade-off discussions, roadmap decisions and technical governance boards. Coordinate with enterprise, BSS, OSS, cloud, security, data and AI stakeholders to resolve dependencies. Support estimates, delivery sequencing, risk mitigation, vendor alignment and executive-level technical communication.
Mandatory Skills
Extensive telecom architecture experience across OSS and BSS Azure Synapse and modern cloud data platform architecture Python and SQL working depth
Enterprise data modeling and canonical integration patterns AI/ML platform architecture and data lifecycle understanding Security, governance, resilience, performance and cost design Strong onsite consulting, facilitation and executive communication
Preferred Skills
Azure architecture certification
Databricks, Microsoft Fabric, streaming or MLOps exposure TM Forum standards and large-scale modernization experience
Key Deliverables
Target-state Data and AI architecture blueprint
Transition roadmap and architecture decision register
Integration, security, governance and operational patterns
Reference designs and non-functional requirement catalogue
Design review outcomes, risk treatments and executive architecture updates
Success Criteria
Architecture decisions are approved, traceable and implementable within program constraints.
Teams adopt consistent patterns across data, analytics and AI workstreams.
Non-functional requirements are designed early and validated through delivery.
Material technical risks, dependencies and cost drivers are visible and actively managed.
Key Stakeholder Interfaces
Client business and technology stakeholders; onsite and offshore delivery teams; enterprise architecture, security, governance, engineering, testing, analytics, AI and operations functions, as applicable to the role

Key Skills

Education

Bachelor's degree