Overview
Principal Data Engineer on Autodesk’s Finance Transformation team responsible for designing and maintaining data platforms, pipelines, and products for Finance reporting, analytics, dashboards, and financial decision-making.
What you'll do
- Design, develop, and maintain scalable data pipelines, models, and data products within Snowflake for Finance reporting and analytics.
- Serve as the primary data engineering partner for Finance BI Developers and business stakeholders to translate requirements into scalable data solutions.
- Drive adoption of data engineering best practices including modeling standards, testing frameworks, monitoring, documentation, and governance.
- Design and maintain dimensional models, curated data layers, and reusable datasets for reporting and self-service analytics.
- Improve data quality, reliability, performance, and maintainability across Finance-owned data products and pipelines.
- Align Finance solutions with broader architectural standards at Autodesk by partnering with engineering teams.
What you'll need
- Expert-level proficiency with Snowflake and modern cloud data warehousing concepts.
- 8+ years of experience in data engineering, data warehousing, or analytics engineering roles.
- 8+ years of experience developing and maintaining ETL/ELT pipelines and enterprise-scale data solutions.
- Strong proficiency with SQL and experience with modern data engineering tools and frameworks for orchestration, transformation, and automation.
- Strong understanding of dimensional modeling and data warehousing concepts.
- Experience leading projects for data quality controls, testing frameworks, monitoring, and operational best practices.
Details
- In-person onboarding and/or in-person ID verification may be required.
Nice to have
- Experience supporting Finance, Sales Finance, Revenue Operations, or FP&A organizations within a subscription-based software or SaaS business.
- Significant Technical Lead experience.
- Experience with BI and analytics platforms such as Power BI, Tableau, or Qlik Sense.
- Hands-on experience building applications that leverage Large Language Models.
- Familiarity with emerging AI application architectures including agentic workflows and AI Skills, Model Context Protocol (MCP) servers.
- Experience using AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor.
