Senior Software Developer- Data Delivery

Job Requisition ID #

26WD98153

Position Overview

The Forma Data Delivery group at Autodesk is looking for a Senior Software Developer to join its team and help build reliable, scalable data products for the construction industry. We are looking for a hands-on engineer with strong data engineering fundamentals, sound software development practices, and experience building production-grade cloud data services.

You will be part of an agile group of talented and self-motivated individuals building highly scalable, secure, and distributed services in the customer-facing analytics and data visualization space. You will work in a global team and collaborate with remote colleagues across design, product, engineering, and support. This role is focused on delivering data pipelines, data models, and platform capabilities that make high-quality data available to Autodesk products and customers.

• Design, build, test, and operate scalable data pipelines, data services, and data delivery workflows in a cloud environment
• Develop and maintain analytics-ready and AI-ready datasets by creating well-structured data models, metadata, documentation, business definitions, and governed access patterns that support reporting, analytics, and future intelligence use cases
• Contribute to technical designs for data-intensive systems with attention to performance, maintainability, resiliency, security, and cost efficiency
• Implement production-quality code and infrastructure changes using modern software engineering practices, including automated testing, code review, CI/CD, and observability
• Improve the quality, reliability, and scalability of data pipelines and data products through monitoring and production-readiness practices
• Collaborate with product owners, engineering managers, architects, data consumers, and peer engineers to clarify requirements and deliver incremental value
• Participate in technical discussions and design reviews, bringing a data engineering perspective to tradeoffs around modeling, storage, processing, and service boundaries
• Improve team engineering standards, documentation, runbooks, and operational practices for data delivery systems
• Mentor less-experienced engineers through pairing, design feedback, and code reviews while continuing to contribute directly to implementation

• 5+ years of professional data engineering experience
• Strong hands-on experience designing, developing, and operating production data pipelines, distributed services, or data platforms in the cloud
• Proficiency with data modeling, data architecture, data storage technologies, and transformation workflows
• Experience with batch and/or streaming data processing, event-driven systems, message queues, and resilient integration patterns
• Strong programming skills in one or more backend/data engineering languages such as Python, Java, Kotlin, Scala, TypeScript, or Go
• Practical experience with SQL and query optimization, including designing data sets for analytics, reporting, or product-facing consumption
• Good understanding of observability for data systems, including monitoring, logging, alerting, tracing, pipeline health, and data quality signals
• Ability to break down ambiguous requirements, propose pragmatic solutions, and deliver independently within a team roadmap
• Strong communication skills with the ability to explain technical tradeoffs to engineers, product managers, and engineering leaders
• Self-motivated, collaborative, empathetic, and accountable, with a bias toward ownership and continuous improvement

• Experience with AWS infrastructure, services, and tools relevant to data engineering and backend services
• Hands-on experience with data orchestration, workflow engines, lakehouse/warehouse patterns, or large-scale object storage
• Experience preparing data assets for analytics, AI/ML, LLM, or internal intelligence use cases through structured datasets, metadata, documentation, and governed access patterns
• Experience with API architecture and data access patterns for internal platforms or external product integrations
• Security knowledge and understanding of common SaaS data-handling risks and mitigation patterns
• Demonstrated ownership of production services, including incident response, post-incident learning, and operational improvements
• Strong attention to detail, especially around data correctness, edge cases, and production readiness