Platform Engineering Lead- Data Services & Reporting

Overview We are seeking a highly accomplished, hands-on SVP – Platform Engineering Lead to be based in Pune. In this critical engineering leadership role, you will provide engineering leadership, technical governance, and architectural alignment across our enterprise-wide Data and Reporting landscape . This is a senior, highly technical engineering leadership and platform delivery role designed for a seasoned platform architect or software engineer. It is not a program management or PMO role. This position serves as a critical bridge that cuts across the entire data and reporting lifecycle —from raw data sources and federated query engines to the presentation, business intelligence, and AI-enabled experience layers. You will hold complete design authority and technical governance over a platform that has a global footprint and affects business users and applications worldwide. You will be responsible for defining and driving our unified platform engineering strategy, standards, and best practices. Working in close partnership with our Enterprise Architecture teams and the wider department's AI design groups, you will lead the effort to rationalize our current platform landscape, ensuring that our data virtualization capabilities, APIs, and reporting engines are integrated into a cohesive, highly scalable, resilient, and sustainable end-to-end global ecosystem. A core expectation of this role is the active adoption and promotion of generative AI tools (such as GitHub Copilot, Claude, and other developer productivity tools) to significantly accelerate software delivery, automate infrastructure-as-code, and elevate technical design quality across the entire data and reporting stack.

1. Cross-Cutting Platform Engineering Strategy & Alignment End-to-End Strategic Roadmap: Define and execute the long-term platform engineering strategy and technical roadmap that seamlessly integrates the enterprise Data Services and Reporting platforms, ensuring strict alignment with enterprise standards. Landscape Rationalization: Review, analyze, and hands-on rationalize the entire platform landscape—consolidating both backend data processing layers and frontend business intelligence/reporting inventories to eliminate redundant capabilities, reduce technical debt, and drive operational efficiency. Enterprise Architecture Partnership: Serve as the primary technical liaison with the broader Technology organization and Enterprise Architecture / Common Architecture Groups , partnering closely to translate enterprise-level blueprints into scalable, high-performance platform implementations. Unified Semantic & Metric Layer: Establish and enforce standards for a centralized, unified semantic layer that bridges federated query engines directly with BI platforms, ensuring consistent business metrics and a "single source of truth" from database to dashboard. AI-Accelerated Platform Delivery: Champion and drive the adoption of generative AI tools (e.g., GitHub Copilot, Claude, ChatGPT) across platform engineering teams to accelerate software development, automate schema and infrastructure-as-code generation, and streamline system refactoring. 2. Platform AI Capabilities & Wider AI Alignment AI Capability Definition & Design: Actively work to define, architect, and design the core platform's AI and conversational query capabilities (including natural language interfaces and autonomous agent infrastructures). Departmental AI Coordination: Partner in close coordination with the wider department's AI architecture and design groups to ensure all conversational and agentic AI deliverables are seamlessly integrated, interoperable, and fully aligned with global AI patterns and security guardrails. 3. Production Estate Management & Technical Debt Elimination Production Monitoring Standards: Enforce rigorous estate management standards by ensuring each application team designs and implements comprehensive, real-time production