Principal Data Engineer -General application

Autodesk is a company that creates software for innovative projects including green buildings, clean cars, smart factories, and hit movies. The role is within their Global Delivery Team in the Enterprise Customer Success Department, focused on improving data and data pipeline architecture.

• 5+ years of experience in a Data Engineer role
• Degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field
• Strong command of English
• Technical skills including AWS (Batch, CloudFormation, IAM, Lambda, ECS, StepFunctions), Terraform, Python, SQL, relational databases (e.g., PostgreSQL), API (RESTful), and BI tools like QuickSight

Nice to have:

• Familiarity with Agile and SCRUM methodologies
• Experience with PowerBI for dashboard development
• Analytical skills working with unstructured datasets
• Experience processing large, disconnected datasets
• Experience with agile, globally distributed teams
• Experience designing and developing prompts for NLP and AI applications
• Collaboration with product teams, data scientists, and engineers to translate user needs into effective prompts
• Skills in analyzing and iterating on performance metrics and user feedback
• Conducting experiments and research to optimize workflows
• Staying up to date with advancements in NLP and AI
• Clear documentation and communication for technical and non-technical audiences

• Create data pipeline architecture
• Assemble complex data sets that meet functional and non-functional requirements
• Identify, design, and implement process improvements including automation and infrastructure redesign
• Build extraction, transformation, and loading infrastructure utilizing SQL and AWS big data technologies
• Develop analytics tools for insights into employee experience and operational efficiency
• Assist team members with data-related technical issues and infrastructure support
• Build processes supporting data transformation, metadata, dependency, and workload management
• Stay updated on cloud provider feature-sets (such as AWS) and apply improvements
• Collaborate with data scientists and analysts to enhance data system functionality