Data Architecture Senior Tech Lead

**Join Us** ----------- At Vodafone, we’re not just shaping the future of connectivity for our customers – we’re shaping the future for everyone who joins our team. When you work with us, you’re part of a global mission to connect people, solve complex challenges, and create a sustainable and more inclusive world. If you want to grow your career whilst finding the perfect balance between work and life, Vodafone offers the opportunities to help you belong and make a real impact. **Role Purpose** ---------------- To design a unified, scalable, and secure Data blueprint in conjunction with tech group to integrate scattered NW and Business Data . This role ensures that all Data project is in align business vision while maintaining a unified standard across all departments to avoid Data silos. **Role Profile** ---------------- * **Technology Stack Ownership and Alignment with Data Group Technology:** Lead the evaluation and selection of all Data tools and ensure group approval for these tools (e.g., choosing the Data Virtualization tools, choosing the Cloud provider, or the Data Lake technology). Also Responsible for reporting to the Group Architecture Maturity Index & initiating any project to meet it. * **Enterprise Data Transformation:** Assess current and planned Architecture and identify Gaps towards a trusty data and work on high-level design for these projects to ensure engagement of Business and Governance – during the project, s/he will act as principal architect responsible for ensuring that objectives, KPIs are going to be achieved * **Data Infrastructure Readiness for AI :**Preparing the data foundation so that internal data scientists can safely use data in their models to learn. If Any discovered gaps, he should put roadmap to close. * **Architectural Enablement for "Intelligent Data Apps" :** The architect must ensure the data infrastructure perfectly supports “Intelligent Data SW products” that adds more analytical insights for Business (IN & Out Data Flow ) and future proof Visualization tools. * **Business Alignment & Governance & Lifecycle Management:** The architect ensures technology roadmap serves business demand and maintains the long-term health of the data ecosystem. **Responsibilities** -------------------- Technology Stack Ownership and Alignment with Data Group Technology: * Negotiate with Group Architecture on yearly plan and budget * Responsible for evaluating SOA and identifying Gaps to be Studied and budgeted for so that it can be implemented * Cross-Domain Domain Modeling Alignment: Collaborate with group architecture to review data attributes across complex domains (e.g., Network Core, BSS/OSS, Retail transactions), ensuring schemas are optimized for high-performance processing inside your Intelligent Data Apps. 2.Enterprise Data Transformation * Assess current and planned Architecture and identify Gaps towards a trusty data and work on these projects till it is transferred from a GAP to project with initial study document with High level design/KPIs and budget – during the project, s/he will act as principal architect responsible for low level design and ensuring that objectives and KPIs are going to be achieved + Introducing Advanced Data Virtualization strategies (e.g., Trino, Denodo) to sit on top of all data sources, to allow real-time, ensuring low-latency querying across without moving raw data unnecessarily) + Data Lakehouse Evolution: Serve as the Principal Architect for migrating the company’s legacy Big Data (Hadoop/On-Prem) estate to a modern, cloud/hybrid Data Lakehouse environment (e.g., Databricks, Snowflake, Delta Lake). Design the multi-phase migration roadmap, Focus on decoupling compute/storage separation and concurrency standards. * Data Governance Integration: Architect automated hooks within the Lakehouse migration so that data metadata, schemas, and lineage are automatically captured by the governance framework at ingestion. * DaaS Evolution: Review and expand the existing DaaS (API) infrastructure ( Document Blueprint for the underlying current/new architecture that exposes data assets via secure, high-speed APIs, allowing developers to query enterprise data) 3. Data Infrastructure Readiness for AI *Preparing the data foundation so that internal data scientists and external enterprise AI clients can safely consume your telecom data core.* * Semantic Layering for GenAI ("Chat with Data"): Architect the semantic data layer and Vector/Graph database integration guidelines. This allows LLM applications to securely map natural language queries directly to your mature DaaS APIs, grounding the AI and preventing data hallucinations. * Data Architecture for Autonomous Agents: Design high-concurrency, immutable data snapshot patterns. This ensures that when multi-agent AI systems query network or commercial data simultaneously, they work from a consistent, synchronized state without causing database deadlocks or performance lags. * AI Data-Sourci