Job Requisition ID #26WD98153
Position OverviewThe 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.
As a senior developer, you will own meaningful technical work, contribute to design decisions, help mentor other engineers through code reviews and technical guidance, and partner with senior technical leaders on architecture where appropriate. You are expected to be highly autonomous within your team scope while escalating risks, tradeoffs, and cross-team dependencies clearly.
Responsibilities- 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
Minimum Qualifications- 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
Preferred Qualifications- 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
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