Principal Data Engineer

Job Requisition ID #

26WD96751

Position Overview

Autodesk is seeking a Principal Data Engineer to lead the design, architecture, and evolution of enterprise-scale data platforms and pipelines. This role is pivotal in shaping Autodesk's data ecosystem, enabling scalable, high-performance data solutions that power analytics, AI/ML, and business decision-making.

You will operate as a technical leader and architect, partnering with cross-functional stakeholders-including Data Science, AI/ML, Product, Finance (FinOps), and Engineering-to deliver robust, scalable, and cost-efficient data solutions. This role demands deep expertise in distributed data systems, cloud data platforms, and modern data architecture patterns, combined with strong leadership and mentoring capabilities.

Roles and Responsibilities

Technical Leadership & Architecture

• Lead the end-to-end design and implementation of scalable data architectures and pipelines (batch + real-time)
• Define and drive data engineering strategy and roadmap aligned with Autodesk's enterprise data platform vision
• Architect solutions leveraging AWS, Azure, Snowflake, and modern data stack tools
• Design and optimize data models, lake architectures, and data mesh frameworks for analytics and ML use cases

• Build and maintain high-performance, resilient, and cost-optimized data pipelines
• Implement real-time and streaming architectures using frameworks such as Spark, Kafka, or Kinesis
• Develop robust ETL/ELT pipelines and data integration workflows using tools like Airflow, Fivetran
• Establish best practices for CI/CD, testing, and deployment of data pipelines

• Define and enforce standards
• Data quality and validation
• Observability and monitoring
• Data governance and lineage
• Implement automated anomaly detection and pipeline optimization using AI/ML techniques

• Partner with data scientists, analysts, product managers, and engineering teams to translate business requirements into scalable solutions
• Drive data-driven decision-making by delivering reliable and accessible data products
• Collaborate with FinOps teams to ensure cost efficiency of data infrastructure

• Lead and mentor a team of data engineers, fostering engineering excellence and innovation
• Influence and guide architecture decisions across multiple teams and domains
• Promote a culture of best practices, scalability, and operational excellence

• Stay current with emerging trends in data engineering, AI, and cloud technologies
• Drive adoption of modern data paradigms (data mesh, lakehouse, hybrid/multi-cloud)
• Continuously evolve tooling, frameworks, and engineering practices

• 10+ years of experience in data engineering or distributed data systems
• 3+ years in a technical leadership or principal-level role
• Proven experience designing and delivering large-scale, enterprise-grade data platforms
• Strong expertise in SQL and Python, Data modelling and architecture design
• Hands-on experience with Cloud platforms (AWS, Azure) Data warehousing (Snowflake or equivalent)
• Experience with batch and streaming frameworks (Spark, Kafka, Hadoop, Kinesis)
• Deep understanding of ETL/ELT paradigms and modern data stack
• Experience with orchestration and ingestion tools (Airflow, Fivetran)
• Strong communication, stakeholder management, and leadership skills

• Experience with Data Lakehouse architectures and Data Mesh principles
• Exposure to AI/ML pipelines and production ML systems
• Experience with multi-cloud or hybrid cloud strategies
• Background in FinOps or cost optimization of data platforms
• Strong foundation in Distributed systems, Algorithms and scalability
• Track record of driving innovation and engineering best practices
• Experience mentoring senior engineers or leading distributed teams

• Strategic Thinking & System Design
• Scalable Data Architecture
• Engineering Excellence & Best Practices
• Leadership & Mentorship
• Stakeholder Communication
• Innovation & Continuous Learning