Autodesk
PrincipalDataEngineer,UserSuccess
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optimal for Principal candidates.
“Principal Data Engineer, User Success at Autodesk. Skills: Data Engineering, AI-native experiences, Agentic insights, LLM ecosystems. Architect batch pipelines. Implement batch pipelines”
What They're Looking For.
Must Have
10+ years data engineering, 10+ years data platform engineering, 10+ years distributed systems, hands-on Python, hands-on Spark, hands-on PySpark, hands-on advanced SQL, hands-on scripting, LLM ecosystems experience, embeddings experience, vector databases experience, Retrieval-augmented generation experience, Agent frameworks experience, orchestration systems experience, streaming technologies experience, analytics engineering knowledge, semantic layer tools knowledge, data governance experience, data lineage experience, cataloging systems experience, product analytics exposure, experimentation frameworks exposure, design reliable ETL/ELT pipelines, operate reliable ETL/ELT pipelines, modern data platforms experience, hands-on AWS services, lead cross-functional technical initiatives, influence architecture, define engineering standards, mentor engineers, communication skills
Nice to Have
product telemetry experience, clickstream data experience, behavioral analytics experience, experimentation platforms experience, ingestion tools experience, orchestration tools experience, transformation tools experience, partner with product teams, partner with design teams, partner with research teams, partner with analytics teams, partner with ML teams, support LLM workflows, support RAG workflows, support agentic AI workflows, support intelligence workflows, modernizing data infrastructure experience
What You'll Do.
Architect batch pipelines
Implement batch pipelines
Architect streaming pipelines
Implement streaming pipelines
Operationalize feature engineering
Operationalize feature stores
Operationalize RAG systems
Operationalize evaluation pipelines
Ensure data observability
Guide build vs. buy decisions
Enable analysts with datasets
Enable product teams with datasets
Translate product questions
Improve instrumentation strategy
Support self-service analytics
Support AI-assisted exploration
Collaborate across Product
Collaborate across Engineering
Collaborate across Data Science
Collaborate across Research
Collaborate across Design
Influence technical direction
Drive alignment on standards
Drive alignment on governance
Drive alignment on best practices
Communicate technical concepts
How You'll Work.
Team & Collaboration
Cross-functional teams; Product teams; Engineering teams; Data Science teams; Research teams; Design teams
Communication Scope
Technical concepts; Non-technical audiences
Full Job Description
**Job Requisition ID #** 26WD97991 **Position Overview** At Autodesk, we do what no other company can: we help our customers design and make anything. The Experience Foundations team at Autodesk plays a critical role in designing the experiences that make that mission a reality, especially in this transformative moment where seamless digital experiences and AI-powered innovation will empower customers and teams to achieve meaningful outcomes faster. The Principal Data Engineer will report to Director of Growth and Data Science in the Experience Foundations organization. This is a critical data science role for our agentic insights platform—we are evolving our data tools and platform to support AI-native experiences, enabling both humans and intelligent systems to better understand user behavior and business impact. As a Principal Data Engineer, you will be driving the design of AI-ready data products that power analytics, machine learning, and emerging agentic experiences and insights and intelligence products. This role requires a balance of deep technical expertise, architectural vision, and cross-functional leadership, influencing how data is structured, governed, and consumed across Autodesk. **Responsibilities** * Architect and implement scale batch and streaming pipelines for large-scale product telemetry with low-latency, high-throughput data access that support LLMs and agentic workflows optimized for: * Real-time and iterative feedback loops * Contextual data access * Retrieval (e.g., embeddings, vector search) * Partner with AI/ML teams to operationalize: * Feature engineering and feature stores * RAG-based systems and evaluation pipelines * Ensure data quality and observability meet the needs of AI-driven decision systems * Guide build vs. buy decisions for data tooling and platforms * Enable analysts and product teams with trusted, well-modeled datasets * Partner with stakeholders to translate product questions into measurable data signals * Improve inst
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