Principal Data Engineer, User Success (agentic Experiences)

Autodesk Autodesk · Enterprise · Toronto, ON +1

Principal Data Engineer focused on building AI-ready data products for agentic experiences and insights. This role involves architecting and implementing data pipelines to support LLMs, RAG, and agentic workflows, ensuring data quality and observability for AI-driven systems, and partnering with AI/ML teams on feature engineering and operationalization.

What you'd actually do

  1. Architect and implement scale batch and streaming pipelines for large-scale product telemetry with low-latency, high-throughput data access
  2. Partner with AI/ML teams to operationalize:
  3. Ensure data quality and observability meet the needs of AI-driven decision systems
  4. Guide build vs. buy decisions for data tooling and platforms
  5. Enable analysts and product teams with trusted, well-modeled datasets

Skills

Required

  • Python
  • Spark
  • PySpark
  • advanced SQL
  • scripting
  • LLM ecosystems
  • embeddings
  • vector databases
  • Retrieval-augmented generation (RAG)
  • Agent frameworks or orchestration systems
  • streaming technologies (Kafka, Flink, Spark Streaming)
  • analytics engineering
  • semantic layer tools (dbt, metrics stores)
  • data governance
  • lineage
  • cataloging systems
  • ETL/ELT pipelines
  • modern data platforms (Iceberg, Hive, Snowflake, Redshift, Athena)
  • AWS services (EMR, Glue, S3, IAM, Lambda, Step Functions)
  • cross-functional technical initiatives
  • technical and non-technical stakeholders

Nice to have

  • product telemetry
  • clickstream data
  • behavioral analytics
  • experimentation platforms
  • ingestion, orchestration, and transformation tools (Airflow, dbt, Fivetran)
  • supporting LLM, RAG, agentic AI, or internal intelligence workflows in production or enterprise environments
  • modernizing data infrastructure

What the JD emphasized

  • AI-native experiences
  • agentic insights platform
  • AI-ready data products
  • LLMs and agentic workflows
  • RAG-based systems
  • agent frameworks or orchestration systems

Other signals

  • AI-native experiences
  • agentic insights platform
  • AI-ready data products
  • LLMs and agentic workflows
  • RAG-based systems