Position Overview

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 instrumentation strategy to ensure high-quality behavioral 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

• 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

  • Retrieval-augmented generation (RAG)

  • 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

• Experience with product telemetry, clickstream data, behavioral 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

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