Autodesk · Bengaluru, IndiaPrepare for this interview
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Job Requisition ID # 26WD96752 Position Overview Autodesk is a global leader in 3D design, engineering, and entertainment software, empowering innovators to design and make a better world. Our platforms support millions of users across industries including architecture, engineering, construction, manufacturing, and media. The Growth Experience Technology (GET) organization is seeking a Principal Engineer- Data Platform & Backend to lead the evolution of scalable data platforms, data pipelines, and data-intensive backend services powering digital experience, commerce, and customer intelligence platforms. This is primarily a data platform engineering hands-on leadership role, with backend engineering as an important enabling capability. The ideal candidate will own modern data architecture, batch and streaming pipelines, data models, data quality, governance, and cloud- scale data systems, while also designing APIs, services, and integrations that make trusted data accessible, reliable, and product-ready. This is a high-impact technical hands-on leadership role responsible for defining architecture, scaling data platforms, and enabling AI-driven personalization, experimentation, customer intelligence, and decision intelligence at global scale. You will operate across multiple teams as a technical authority, mentor, and strategic driver, shaping both execution and long-term technical vision. Roles & Responsibilities Lead the architecture and implementation of scalable enterprise data platforms, data pipelines, and data products. Design and build batch, real-time, and event-driven data processing systems. Own data architecture patterns including data modeling, lakehouse design, data contracts, schema evolution, lineage, quality, and governance. Build reliable data ingestion, transformation, orchestration, and serving layers for digital experience, commerce, customer intelligence, analytics, operational reporting, and AI/ML readiness. Design data-intensive backend services, APIs, and integrations that expose trusted data to products, platforms, and internal consumers. Define standards for ETL/ELT, streaming, data validation, observability, monitoing, metadata management, and pipeline reliability. Work with cloud and data platforms such as AWS, Azure, Snowflake, Databricks, Delta Lake, Apache Iceberg, Spark, Kafka/Kinesis, Airflow, dbt, and Fivetran. Partner with Engineering, Product, Data Science, Analytics, DevOps, Security, and FinOps teams to translate business and platform needs into scalable data solutions. Optimize data systems for performance, reliability, cost, security, and operational excellence. Contribute to technical roadmap planning, platform modernization, technical debt reduction, and adoption of modern data architecture patterns. Mentor engineers and raise the engineering bar across data design, implementation, testing, observability, operations, and ownership.
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