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 qual ity, 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
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Lead the architecture and implementation of scalable enterprise data platforms, data pipelines, and data products.
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Design and build batch, real-time, and event-driven data processing systems.
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Own data architecture patterns including data modeling, lakehouse design, data
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contracts, schema evolution, lineage, quality, and governance.
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Build reliable data ingestion, transformation, orchestration, and serving layers for
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digital experience, commerce, customer intelligence, analytics, operational reporting, and AI/ML readiness.
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Design data-intensive backend services, APIs, and integrations that expose trusted
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data to products, platforms, and internal consumers.
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Define standards for ETL/ELT, streaming, data validation, observability, monitoring,
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metadata management, and pipeline reliability.
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Work with cloud and data platforms such as AWS, Azure, Snowflake, Databricks,
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Delta Lake, Apache Iceberg, Spark, Kafka/Kinesis, Airflow, dbt, and Fivetran.
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Partner with Engineering, Product, Data Science, Analytics, DevOps, Security, and
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FinOps teams to translate business and platform needs into scalable data solutions.
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Optimize data systems for performance, reliability, cost, security, and operational
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excellence.
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Contribute to technical roadmap planning, platform modernization, technical debt
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reduction, and adoption of modern data architecture patterns.
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Mentor engineers and raise the engineering bar across data design, implementation, testing, observability, operations, and ownership.
Minimum Qualifications
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9+ years of hands-on engineering experience, with strong experience in data
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engineering, data platforms, or distributed data systems.
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3+ years in a technical leadership role such as Principal Engineer, Staff Engineer,
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Architect, or Tech Lead.
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Proven experience designing and operating large-scale data platforms, pipelines, and data-intensive systems.
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Strong expertise in SQL, Python, data modeling, distributed systems, and cloud
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data architecture.
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Hands-on experience with modern data platforms such as Snowflake, Databricks,
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Delta Lake, Apache Iceberg, or equivalent.
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Experience with batch and streaming technologies such as Spark, Kafka, Kinesis,
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Flink, or equivalent.
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Experience with orchestration and transformation tools such as Airflow, dbt,
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Fivetran, Dagster, or equivalent.
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Good backend engineering experience with APIs, microservices, event-driven
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architecture, and service-to-data platform integrations.
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Experience with cloud platforms such as AWS and/or Azure.
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Strong understanding of data quality, governance, lineage, observability, schema
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evolution, CI/CD, and production operations.
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Ability to make pragmatic architecture decisions that balance scalability, reliability,
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delivery speed, and cost.
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Strong communication, stakeholder management, mentoring, and technical leadership skills.
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Bachelor’s degree in Computer Science, Engineering, or equivalent practical
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experience.
