Data Engineer (Python, Spark)

Autodesk is looking for diverse engineering candidates to join the Access domain, building data Engineering pipelines leveraging data platforms. As a Data Engineer, you will act as a Data Champion, driving data quality, reliability, and observability standards across platforms. You will rapidly improve critical data processing and analytics pipelines while solving hard problems around reliability, resiliency, and scalability. 

Our tech stack includes Hive, Spark, Flink, Presto, Iceberg, Looker, Power BI, and cloud services on Amazon Web Services, with data platform components. Analytics and reporting leverage platforms from Snowflake, Google (Looker), and Microsoft (PowerBI). 

Roles and Responsibilities

• You will need a product-focused mindset. It is essential for you to understand business requirements and architect systems that scale and extend to accommodate those needs

• Break down complex problems, define technical solutions, and sequence work to enable fast, iterative improvements

• Design, build, and maintain scalable data pipelines and data models across Access

• Modernize legacy data workflows and infrastructure, including migrations from platforms such as Hive to Iceberg 

• Develop reliable ETL/ELT workflows to ingest, transform, and serve data for analytics and operational use cases

• Interface with data engineers, data scientists, product managers, and other stakeholders to understand their needs and promote best practices 

• You have a growth mindset. You will identify business challenges and opportunities for improvement and solve them using data analysis and data mining to make strategic and tactical recommendations 

• Enable analytics and provide critical insights around product usage, campaign performance, funnel metrics, segmentation, conversion, and revenue growth

• You will partner with different teams within the organization to understand business needs and requirements

• Own critical data pipelines end-to-end and contribute to improving the overall data platform

Minimum Qualifications 

• 3–5 years of experience in data engineering or related roles

• Strong proficiency in SQL and a programming language such as Python 

• Experience building data pipelines using modern data technologies (e.g., Spark, Airflow, Snowflake, or similar) 

• Experience with cloud-based data architectures (AWS, Azure, or GCP)

• Experience building dashboards and analytics in Looker and/or Power BI 

• Experience with version control and CI/CD tools like Git and Jenkins CI

• Experience with streaming architectures and Flink-based processing

• Strong understanding of data modelling, pipeline reliability, and large-scale data processing 

• Experience working with notebook solutions like Jupyter, EMR Notebooks, or Apache Zeppelin 

• Experience leveraging AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) to improve productivity 

• Strong problem-solving and communication skills 

• Bachelor's degree in computer science, Engineering, or related field, or equivalent practical experience
   

Preferred Qualifications

• Exposure to building or using AI-powered tools, agents, or automation in data workflows 

• Familiarity with concepts like Model Context Protocol (MCP) or similar approaches for integrating AI with data systems 

• Experience working on data platform enhancements, optimizations, or small-scale migrations 

• Understanding of data observability and monitoring practices 

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