Platform Engineering Lead- Data Services & Reporting

Role 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.
Key Responsibilities1. 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 monitoring, observability, and alerting tools (e.g., Splunk, Prometheus, Grafana, AppDynamics, or equivalent).
Tech Debt Prevention: Proactively drive platform patterns that simplify operations, ensure ease of production estate management, and systematically eliminate and prevent the incurrence of technical debt across the application lifecycle.
4. Data Virtualization & Federation Industrialization
Query Federation Industrialization: Lead the enterprise-scale industrialization of our query federation and data virtualization capabilities, establishing logical data access patterns that allow real-time query execution across dozens of heterogeneous catalogs without physical data movement.
Standardized APIs & Data Access Patterns: Design and implement standardized, highly secure APIs and reusable data access patterns (leveraging Java/Spring Boot frameworks) to support high-performance, real-time data consumption by downstream reporting and analytical engines.
Centralized Security & Entitlements: Enforce robust governance controls, row/column-level data masking, and fine-grained access controls (e.g., Apache Ranger) across the virtualization layer to ensure secure data delivery to all reporting consumers.
5. Reporting Platform & Experience Layer Integration
Reporting Platform Architecture: Modernize the reporting and business intelligence infrastructure, ensuring that high-concurrency BI platforms (e.g., Tableau, custom web-based dashboards, and automated document generation engines like Aspose) are optimized to query virtualized data structures in real time.
High-Throughput Performance Tuning: Optimize query performance and end-to-end latency across the entire stack—from the query federation engine down to the frontend visualization layer—enabling instant, interactive dashboards and conversational data search.
6. Technical Leadership & Mentorship
Technical Mentorship: Provide strong technical leadership, architectural guidance, and mentorship to a global team of platform engineers, data architects, and reporting developers.
Culture of Innovation: Foster a high-performance engineering culture focused on automation, continuous integration/continuous delivery (CI/CD), and modern platform engineering practices.
Technology Skills & CompetenciesRequired Technical Skillsets (Must be Hands-on)
Java Software Engineering (Core Competence): Advanced, hands-on expertise in Java and enterprise Java frameworks (specifically Spring Boot, Spring Framework, and Hibernate/JPA). Proven track record of architecting, reviewing, and governing high-performance backend microservices, robust platform integration layers, and custom API layers.
Data Federation & Query Optimization (Required Concepts): Deep understanding of distributed query execution, query plans, pushdown optimization, and logical data fabric/data virtualization concepts.
BI & Reporting Platform Engineering: Strong architectural knowledge of enterprise BI and reporting platforms (e.g., Tableau, custom JavaScript/React web dashboards, Aspose, or similar reporting and document generation engines).
AI-Assisted Development (Must-Have): Proven capability and hands-on experience using generative AI tools (e.g., GitHub Copilot, Claude, ChatGPT, or equivalent) to accelerate software delivery, write platform code, generate tests, and optimize development workflows.
Enterprise Architecture & Implementation Design: Proven track record of designing and implementing high-scale, distributed, and resilient enterprise architectures bridging data storage, virtualization, APIs, and reporting in alignment with enterprise standards.
API Design & Governance: Strong expertise in designing standardized REST/gRPC APIs, microservices, and secure data access patterns.
Database Engineering & Security: Deep knowledge of relational databases (Oracle, SQL Server), distributed storage systems, and centralized data access governance frameworks (e.g., Apache Ranger).
Preferred / Nice-to-Have Skillsets
Agentic AI & LLM Engineering (Highly Preferred): Conceptual or hands-on experience with Agentic AI frameworks and libraries (such as LangChain, LangGraph, Google ADK, or equivalent) to design, architect, and orchestrate autonomous AI agents, prompt engineering pipelines, and Retrieval-Augmented Generation (RAG) architectures.
Data Virtualization Platforms (Highly Preferred): Deep hands-on or design experience with query federation engines such as Starburst, Trino, Presto, Denodo, Dremio, AWS Athena, or Apache Drill.
AI Platform Engineering & LLM Architecture: Conceptual or hands-on understanding of engineering platform infrastructure to support AI workloads, integrating Large Language Models (LLMs), managing prompt engineering pipelines, vector databases, and deploying model hosting platforms.
Modern Lakehouse Platforms: Experience working with cloud-native enterprise data lakehouse or warehouse platforms (e.g., Databricks, Snowflake, or equivalent).
Distributed Compute Engines: Familiarity with Apache Spark (PySpark, Spark SQL) or Apache Flink for large-scale data processing.
CI/CD & DevOps: Hands-on experience with modern DevOps toolchains (Jenkins, Tekton,