Software Engineering Manager, Data Platform

Job Requisition ID # 26WD97080 Position Overview We are looking for an experienced Engineering Manager to lead our Batch Processing platform team — responsible for building and scaling a high-throughput, governed data platform that powers critical business decisions across the organization, including Autodesk's AI products such as Autodesk Assistant. This role sits at the intersection of high-scale platform engineering, data architecture, and organizational leadership. You will lead a team of strong engineers designing and operating large-scale distributed data processing systems on a modern lakehouse architecture, enabling reliable, secure, and cost-efficient data movement at petabyte scale. Responsibilities Team leadership & delivery Lead, mentor, and grow a team of strong engineers working on batch processing and data platform capabilities Be an AI-native leader — leverage AI tools to drive rapid iteration, experimentation, and continuous product evolution Drive roadmap execution with a focus on reliability, scalability, ease of use, and time-to-data Establish a high-ownership culture with strong engineering fundamentals: design reviews, RCA, testing, and observability Partner with TPMs and stakeholders to ensure predictable, high-quality delivery Platform architecture & engineering Design and evolve a scalable, AI-friendly batch processing platform using Apache Spark (PySpark), distributed compute frameworks, workflow orchestration (Airflow / Temporal), Kubernetes controllers, and AI agents Build systems capable of processing billions of records at petabyte scale Enable self-service and AI-native pipeline capabilities for internal teams through clean, extensible APIs and platform abstractions Optimize continuously for performance, cost efficiency, and reliability Lakehouse & data ecosystem Drive adoption of a modern lakehouse architecture (S3 Apache Iceberg or equivalent) Build self-service batch processing capabilities that enable internal teams to run Spark, Python, Ray, and MLOps jobs with minimal friction and strong platform guardrails Enable downstream consumption via SQL engines and warehouse integrations Governance, security & compliance Ensure platform adherence to data governance and security standards Build self-service controls and abstractions that allow teams to manage access, enforce data policies, and meet compliance requirements without deep platform expertise Partner with security and compliance teams to meet enterprise and regulatory requirements Observability & reliability Build end-to-end observability: metrics, logging, alerting, SLA tracking, and reporting Drive incident management, root cause analysis, and continuous improvement Improve platform resilience through automation and resilience patterns Stakeholder & cross-team collaboration Partner with data engineering, analytics, and platform teams to drive adoption Work closely with infra, security, and partner teams for platform integration Influence architecture decisions at the org level Communicate effectively with senior leadership and business stakeholders Minimum qualifications 8–12 years in software or data engineering 3–5 years managing engineering teams Experience leading platform teams or large-scale data systems Strong hands-on experience with distributed data processing (Spark, Flink, etc.), batch pipelines at scale, and AWS (preferred) Deep understanding of data lake / lakehouse architectures, data modeling, and workflow orchestration (Airflow, Temporal, etc.) Proven ability to hire, mentor, and grow high-performing teams Strong communication and cross-functional influence skills; ability to balance technical depth with strategic thinking Preferred qualifications Experience with Apache Iceberg or Delta Lake Familiarity with data observability tools Experience with metadata and catalog systems (e.g., Atlan or similar) Exposure to data governance frameworks and compliance standards #LI-SB4 Lea ... (truncated, view full listing at source)