Manager, Data Engineering

Autodesk has invested in data-driven decision-making, optimizing our platform’s business, product, and cost. The next five years will transform how we work, with a surge in data volume and scaling of AI/ML to provide even greater value to our customers. Reporting to the Director, Insights and Analytics, the Data Engineering Manager will build and lead a team focused on building scalable, reliable, and high-quality data pipelines and processes that power strategic analytics, data science and cost optimization activities across Autodesk's platform initiatives.
* Lead and mentor a team of data engineers responsible for developing and maintaining data pipelines and infrastructure on platforms such as AWS and Snowflake
* Define and drive the long-term vision for data engineering in alignment with Autodesk data platform strategy and analytics roadmap
* Partner with analysts, data scientists, Fin Ops engineers, engineering and product teams to understand data needs and translate them into scalable data solutions
* Establish clear standards for operational excellence and define “what good looks like” across all stages of the data lifecycle
* Drive the design and development of data models and ETL/ELT processes that ensure high data quality, availability, and performance
* Champion and implement data engineering best practices, including testing, documentation, governance, and observability
* Leverage AI-driven approaches in data workflows, including anomaly detection, orchestration optimization, and automated code generation
* Manage project timelines, resources, and priorities across multiple initiatives in a fast-paced, cross-functional environ ment
* Stay current with emerging trends in data engineering and proactively evolve the team’s capabilities and toolset
* 10 years of experience in data engineering, with at least 4 years in a leadership or management role
* Demonstrated success leading technical teams and delivering complex data engineering projects
* Experience working with cloud data platforms (e.g., AWS, Snowflake, Databricks) and orchestration tools (e.g., Airflow)
* Proficient in SQL, Python, and data modeling best practices
* Knowledge of big data processing frameworks (e.g. Spark, Hadoop)
* Experience building and maintaining large-scale batch and real-time data pipelines
* Experience with both ETL and ELT pipelines, including traditional ETL tools (e.g., Airflow, Informatica) and modern ELT frameworks (e.g., dbt, Snowflake)
* Excellent communication and stakeholder management skills
* Drive scalable architecture through platform systems design and modern design patterns
* Experience working in product/platform organizations or supporting data engineering for SaaS-based applications
* Familiarity with data quality frameworks, observability tools, and CI/CD for data
* Knowledge of Fin Ops practices, data security, and compliance in cloud environments
* Knowledge of AWS IAM roles, permissions, and best practices for least-privilege access
* Passion for coaching talent and building inclusive, high-performing teams
* 10 ans d'expérience en ingénierie des données, dont au moins 4 ans dans un rôle de direction ou de gestion
* Expérience avérée dans la direction d'équipes techniques et la réalisation de projets complexes d'ingénierie des données
* Expérience de travail avec des plateformes de données cloud (par exemple, AWS, Snowflake, Databricks) et des outils d'orchestration (par exemple, Airflow)
* Maîtrise de SQL, Python et des meilleures pratiques en matière de modélisation des données
* Connaissance des cadres de traitement des mégadonnées (par exemple, Spark, Hadoop)
* Expérience dans la création et la maintenance de pipelines de données à grande échelle, par lots et en temps réel
* Expérience avec les pipelines ETL et ELT, y compris les outils ETL traditionnels (par exemple, Airflow, Informatica) et les cadres ELT modernes (par exemple, dbt, Snowflake)
* Excellentes compétences en communication et en gestion des parties prenantes
* Capacité à mettre en œuvre une architecture évolutive grâce à la conception de systèmes de plateformes et à des modèles de conception modernes
* Expérience dans des organisations de produits/plateformes ou dans le soutien à l'ingénierie des données pour des applications SaaS
* Connaissance des cadres de qualité des données, des outils d'observabilité et du CI/CD pour les données
* Connaissance des pratiques Fin Ops, de la sécurité des données et de la conformité dans les environnements cloud
* Connaissance des rôles, des autorisations et des meilleures pratiques AWS IAM pour un accès avec le moins de privilèges possible
* Passion pour l'accompagnement des talents et la constitution d'équipes inclusives et performantes
#J-18808-Ljbffr