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Specialized data-platform skills reduce applicant pool despite Autodesk brand and managerial visibility.
Medium — core data-platform skills are transferable, but lakehouse, governance, and petabyte-scale experience add specialization.
High — explicit 8–12 years, 3–5 years management, plus specific distributed-data platform tech required.
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Lead and grow a team responsible for a high-throughput, governed batch processing data platform enabling critical business decisions including AI product data needs.
Own platform architecture and engineering of scalable distributed data processing systems (e.g., Spark, Airflow) processing petabyte-scale data on modern lakehouse architecture.
Ensure platform reliability, governance, security, compliance, and observability with strong collaboration across multiple technical and business stakeholders.
8–12+ years in software or data engineering with 3–5+ years managing engineering teams.
Experience with distributed data processing technologies (Spark, Flink), batch pipelines at scale, and AWS preferred.
Deep understanding of data lake/lakehouse architectures, data modeling, and workflow orchestration (Airflow, Temporal).
Proven ability to hire, mentor, and grow engineering teams; strong communication and cross-functional influence skills.
Experienced leader combining engineering depth with strategic oversight of large-scale batch data platforms.
Comfortable working at the intersection of platform architecture, AI-enabled pipeline development, and organizational leadership.
Proven track record driving adoption of modern lakehouse data ecosystems with a clear focus on reliability, scalability, and compliance in a collaborative environment.