Principal Software Engineer with Java, Spring boot & Azure cloud at NielsenIQ

Principal Software Engineer, Pune We are looking for a Principal Software Engineer who defines and drives the technical strategy for large‑scale, distributed, and cloud‑native systems. This role operates at a company and platform level, designing foundational architectures, enforcing engineering standards, and leading complex technical transformations that impact multiple teams and products. You will act as a technical authority and architect, balancing short‑term delivery needs with long‑term scalability, reliability, security, and cost efficiency. This role requires deep hands‑on expertise in Java, Python, SQL, distributed systems, cloud platforms, and modern DevSecOps practices, along with the ability to guide teams in AI‑driven development and generative tooling adoption. Responsibilities • Define and evolve the technical strategy for large‑scale, distributed, and high‑availability systems • Design foundational platforms, frameworks, and shared services that impact multiple teams • Lead architectural transformations for legacy systems and large‑scale applications • Drive system‑wide decisions with a strong focus on scalability, performance, resilience, security, and maintainability • Translate ambiguous business requirements into robust, future‑proof technical architectures Platform Governance & Engineering Standards •  Develop and enforce governance policies for APIs, security, cloud infrastructure, and system integrations • Define company‑wide best practices for engineering quality, reliability, observability, and efficiency • Establish architectural guardrails to ensure consistent design and implementation across teams • Drive adoption of security, compliance, and infrastructure automation best practices Cloud, DevSecOps & Cost Optimization •  Establish strategies for cost‑efficient cloud resource utilization, scaling, and performance optimization • Architect standardized CI/CD frameworks used across multiple teams • Define DevSecOps guardrails ensuring secure, compliant, and consistent delivery pipelines at scale • Champion Infrastructure‑as‑Code, automation, and operational excellence Data, Streaming & Distributed Systems •  Provide technical leadership in large‑scale data processing, streaming architectures, and messaging systems • Design and evolve systems using Kafka, queues, async processing, and event‑driven patterns • Guide teams on concurrency, fault tolerance, back‑pressure handling, and system resilience AI & Engineering Innovation •  Guide teams in AI‑driven development practices, including the responsible use of Generative AI tooling • Evaluate AI and GenAI technologies for quality, scalability, security, and real business value • Design and integrate AI/ML capabilities into systems from a platform and application engineering perspective • Champion innovations that improve developer productivity, automation, and engineering efficiency Technical Leadership & Collaboration •  Act as a technical mentor and design authority for senior and mid‑level engineers • Engage in deep technical problem‑solving, influencing system‑wide technical decisions • Collaborate closely with Product, Security, Operations, and Platform teams • Lead by example through hands‑on design reviews, architectural guidance, and complex issue resolution Required Technical Skills: Core Engineering  •  Java (Spring Boot, REST, Microservices, JVM internals) • Python (core Python, scripting, automation, data processing) • SQL (advanced querying, schema design, performance tuning) • Strong knowledge of Data Structures, Algorithms, and System Design • Scripting languages (Shell, Bash, or similar) Systems & Platforms • Distributed systems, concurrency, fault tolerance, scalability patterns • Cloud platforms (Azure / AWS / GCP) and cloud‑native architectures • Containers and orchestration (Docker, Kubernetes) • CI/CD, DevSecOps, Infrastructure‑as‑Code • Messaging and streaming systems (Kafka, event‑driven architectures) AI / Data (Working Knowledge) • Experience integrating AI/ML or GenAI APIs into production systems • Understanding of data pipelines, model deployment, and operational AI concerns • Ability to assess where AI adds value versus unnecessary complexity