Senior Data Software Engineer, Personalization (Mumbai)

Senior Data Software Engineer, Personalization Experience: Not Available to Not Available years Location: Toronto, ON, CAN Skills: Python, Java, Kotlin, Spring Boot, FastAPI, Airflow, Prefect, Snowflake, Hive, Redshift, SQL, REST, gRPC, Kafka, Kinesis Job Requisition ID # 26WD97925 Position Overview Do you thrive in a fast-paced, high-energy environment Are you a creative problem solver who enjoys building solutions and products using cutting edge technologies Are you looking to collaborate with motivated individuals from diverse backgrounds Do you have the drive to make things happen If so, you are in the right place. Autodesk is leading the transformation of how users interact with design and engineering software by embedding AI deeply into our products. We are building cloud-native, AI-powered platforms that operate at scale, leveraging data, machine learning, and agentic systems to deliver intelligent, adaptive, and personalized experiences across our flagship products including AutoCAD, Revit, Construction Cloud, and Forma. The Personalized Experiences Team is a centralized Personalization group working closely with product line development teams across the company to democratize ML/Analytics across all Autodesk products. You will design and own the data foundations that power Autodesks personalization Platform capabilities. That means modeling highly complex, high-stakes data, building reliable pipelines and services, and ensuring that downstream product features and intelligence workflows operate with accuracy, consistency, and scale. This is a hands-on, senior engineering role with real ownership. You will work across backend services, data pipelines, and APIs, taking features from design through production. You will help define schemas, transformations, and architectural patterns that become the backbone of the platform as it scales. While the primary focus is backend and data engineering, you are expected to engage pragmatically across the stack to ensure data and intelligence are surfaced correctly in the product. Reporting: You will report to an Engineering Manager within the AI and Personalization organization. Responsibilities Design and build scalable data pipelines to ingest, process, and serve product usage and behavioral data for personalization and AI use cases Develop backend services and data APIs using technologies such as Python, Java, or Kotlin, and frameworks like Spring Boot, FastAPI, or similar Build and operate microservices that expose data and intelligence capabilities to internal and customer-facing applications Define and evolve data models, schemas, and transformations to ensure high-quality and reliable datasets Build systems that support AI and agentic workflows, ensuring data is structured and accessible for automated decision-making and intelligent agents Partner with product managers, data scientists, and analysts to translate business needs into scalable data systems Ensure data quality, observability, and reliability across pipelines and services Contribute to architectural decisions and drive best practices in data and backend engineering Mentor engineers on data modeling, SQL performance, and scalable pipeline design Minimum Qualifications BS or MS in Computer Science, Engineering, or a related field 8 or more years of experience building production-grade software systems Strong experience designing and building backend services and distributed systems using languages such as Python, Java, or Go Experience with API design and development, including REST or gRPC-based services Strong experience designing and operating large-scale data systems and distributed architectures in cloud environments, AWS preferred Deep expertise in SQL and relational data modeling, including schema design, normalization, and performance optimization at scale Strong understanding of data modeling concepts for analytical and operational systems, including building durable, reusable datasets Experience building and operating data pipelines using tools like Airflow, Prefect, or similar Experience working with cloud data platforms such as Snowflake, Hive, or Redshift Strong understanding of data quality, testing, lineage, and monitoring in production systems Ability to design and build scalable systems that serve high-volume data workloads Preferred Qualifications Experience with personalization, recommendation systems, or ML platforms Experience with real-time or event-driven architectures such as Kafka or Kinesis Familiarity with LLM-based systems, including building or supporting data pipelines for AI-driven applications Experience working with or enabling agentic workflows or AI-powered automation Experience collaborating closely with data science or ML teams Experience mentoring engineers or leading technical initiatives Ideal Candidate You are passionate about building data-driven systems that improve .