Lead Software Engineer and take full ownership of large-scale, cloud-native data platforms. You will define engineering standards, drive technical strategy, mentor a talented team, and architect resilient systems that fuel sustained business growth across AWS and global markets. Req.#[redacted] Responsibilities Design and deliver large-scale cloud-native data platforms on AWS using REST APIs, microservices, and event-driven architectures to build highly scalable and resilient systems Work hands-on across a broad technology stack including Java, TypeScript, Angular, Python, and Spark alongside SQL/NoSQL databases to solve complex engineering challenges and maintain platform excellence Lead product-wide technical initiatives focused on performance optimization, scalability, reliability, security, governance, and cost efficiency Partner with global engineering, product management, architecture, and business stakeholders to align technical solutions with strategic business objectives Own the end-to-end software development lifecycle, from requirements gathering and solution design through development, deployment, observability, and documentation Mentor and guide junior engineers, fostering a culture of innovation, accountability, collaboration, and continuous technical excellence Requirements 8 to 10 years of software engineering experience with deep expertise in building scalable, UX-driven applications and distributed systems architecture Proven hands-on proficiency in Java, Angular, Python, and TypeScript with strong command of object-oriented design patterns and functional programming principles Extensive experience with AWS services including S3, Lambda, API Gateway, and EventBridge, combined with Kubernetes containerization and Infrastructure as Code tools such as Terraform and Ansible Solid background in data warehousing, data lakes, Delta Lake architectures, and big data technologies including PySpark and Apache Spark for high-performance distributed data processing Strong knowledge of event-driven messaging technologies such as Kafka, SNS, and SQS, along with relational and NoSQL databases including PostgreSQL, Aurora, DynamoDB, MongoDB, and Redis Demonstrated experience with CI/CD and DevOps practices using Jenkins, GitHub/GitLab, and automated deployment pipelines, paired with robust testing strategies and strong analytical problem-solving skills