Principal Data Engineer, User Success

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 behaviour 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 scalable 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 instrumentation strategy to ensure high‑quality behavioural data
• Support self‑service analytics and AI‑assisted exploration
• Collaborate across Product, Engineering, Data Science, Research and Design
• Influence technical direction without direct authority
• Drive alignment on data standards, governance, and best practices
• Communicate complex technical concepts to both technical and non‑technical audiences

Minimum Qualifications

• 10+ years of experience in data engineering, data platform engineering, distributed systems, or related technical roles, including ownership of large‑scale production data systems
• Strong hands‑on experience with Python, Spark, PySpark, advanced SQL, and scripting
• Experience with LLM ecosystems, embeddings, vector databases
• Experience with retrieval‑augmented generation (RAG)
• Experience with agent frameworks or orchestration systems
• Experience with streaming technologies (Kafka, Flink, Spark Streaming)
• Knowledge of analytics engineering and semantic layer tools (dbt, metrics stores)
• Experience with data governance, lineage, and cataloging systems
• Exposure to product analytics and experimentation frameworks
• Experience designing and operating reliable ETL/ELT pipelines across batch and streaming workloads, including orchestration, validation, backfills, incremental processing, and data quality checks
• Experience with modern data platforms, including Iceberg, Hive, Snowflake, Redshift, Athena, or equivalent technologies
• Hands‑on experience with AWS services, including EMR, Glue, S3, IAM, Lambda, Step Functions, and related cloud‑native infrastructure
• Demonstrated ability to lead cross‑functional technical initiatives, influence architecture, define engineering standards, and mentor engineers
• Strong communication skills with technical and non‑technical stakeholders

Preferred Qualifications

• Experience with product telemetry, clickstream data, behavioural analytics, or experimentation platforms
• Experience with ingestion, orchestration, and transformation tools such as Airflow, dbt, Fivetran, or similar
• Experience partnering with product, design, research, analytics, and ML teams to create data products that directly inform user experiences or power intelligent product capabilities
• Experience supporting LLM, RAG, agentic AI, or internal intelligence workflows in production or enterprise environments
• Track record of modernizing data infrastructure in environments with fragmented systems, evolving requirements, or limited standards