Senior Director, Data Engineering

Job Requisition ID #

Position Overview

Sitting in our Growth Experience Technology organization which supports our GTM/Business Systems and .com technology, we are seeking a Senior Director, Data Engineering to lead Autodesk’s end-to-end data engineering strategy across batch and real-time platforms. This role is accountable for defining how data is adopted and operationalized at enterprise scale to drive measurable business outcomes, not just technical availability. 

This leader will own Autodesk’s GTM data foundations, including warehouse, Batch, and real-time processing by partnering with Platform & Architecture teams, and ensure they enable reliable decision-making, AI-driven use cases, and cost-efficient operations across Finance, Marketing, Sales, Customer Success, and Digital teams(.com). Success in this role is measured by active business adoption, improved decision velocity, and sustained value creation from data investments. 

The role partners closely with the Platform Services and Emerging Technologies (PSET) Infrastructure organization to ensure data engineering solutions are built on secure, scalable, and resilient platforms, while establishing clear governance standards, common data models, and shared metrics that influence how teams across Autodesk build and use data. 

As a senior leader, you will guide a global organization to deliver AI-ready data solutions, shape company-wide data practices, and prioritize investments based on business impact, cost discipline, and long-term scalability. This role requires strong cross-functional leadership, executive-level communication, and a mindset focused on continuously optimizing the value of Autodesk’s data and AI ecosystem. 

This role works with teammates across the globe and includes up to 25% travel for in-person collaboration, conferences, and team offsites. The role is preferred hybrid to our San Francisco office; candidates based outside San Francisco should expect increased travel. 

Responsibilities 

Data Engineering & Architecture  

• Own measurable adoption outcomes by ensuring data solutions are actively used to drive business decisions, including defined success metrics such as active users, decision velocity, revenue impact, and deprecation of low-value data assets

• Lead enterprise data solutions and related AI enablement to deliver monetizable insights through automation, accuracy, and scale

• Accountable for optimizing cost versus business value across data investments, ensuring spend is justified by measurable revenue impact, margin improvement, or operating efficiency—not platform utilization alone

• Lead batch and real-time data engineering across Spark, Databricks, Snowflake, DBT, Nexla, BigQuery  for batch processing and Kafka, Flink, streaming frameworks for real-time processing.

• Collaborate with PSET Infrastructure team to design reliable, scalable platforms while guiding your team to build robust data pipelines, models, and AI-ready data solution using PSET infrastructure

• Evangelize data strategy and best practices across the COO & CFO organization to ensure adoption and alignment.

• Implement and maintain a common data model to standardize definitions across Finance, Marketing, Sales, and Digital Properties (.com) domains

• Ensure standardized metrics and semantic consistency to enable accurate reporting and BI performance

• Define and execute data lake strategies using modern table formats (Apache Iceberg, Delta Lake, Hudi) to support scalability, schema evolution, and cost-efficient storage

• Establish SLA/SLO tiers for batch and streaming data freshness, availability, and quality

• Embed lineage, observability, privacy, and compliance into all data solutions

• Implements AI-enabled analytics to improve insights and decision-making​ that accelerate digital transformation and cross-functional impact

• Partner with Data Science and Product teams to enable AI/ML applications, including personalization, recommendation systems, fraud/risk detection, predictive analytics, and AI-driven workflows

• Provide governed feature stores with consistent online/offline access and freshness guarantees

• Build data pipelines that support finance-grade reconciliations and sales/marketing attribution

Organizational Leadership  

• Lead a multi-level data engineering organization (senior managers/directors, senior ICs) with clear career growth, development, and succession planning

• Define and deliver OKRs focused on adoption, reliability, cost efficiency, and compliance  

• Mentor senior leaders in data engineering, MDM, governance, and