Job Requisition # 25WD89148
French translation can be found below! / La traduction en français se trouve plus bas!
Position OverviewAutodesk 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.
ResponsibilitiesLead and mentor a team of data developers 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 developers, 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
Autodesk a investi dans la prise de décision basée sur les données, optimisant ainsi les activités, les produits et les coûts de notre plateforme. Les cinq prochaines années vont transformer notre façon de travailler, avec une augmentation considérable du volume de données et une expansion de l’IA/ML afin d’offrir encore plus de valeur à nos clients.
Sous la responsabilité du directeur Insights and Analytics, le responsable Data Enablement constituera et dirigera une équipe chargée de mettre en place des pipelines et des processus de données évolutifs, fiables et de haute qualité qui alimenteront les activités d’analyse stratégique, de science des données et d’optimisation des coûts dans le cadre des initiatives de la plateforme Autodesk. Au sein de l’équipe Analytics & Insights (A&I), vous jouerez un rôle essentiel dans la prise de décisions fondées sur les données en créant et en maintenant des pipelines de données fiables, des modèles de données sélectionnés et des ensembles de données prêts à être analysés.
Vous travaillerez en étroite collaboration avec des analystes, des scientifiques des données, des développeurs Fin Ops et des équipes produit afin de créer des solutions qui prennent en charge le reporting, l’expérimentation…
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