We are making the future of Mobility come to life starting today.

At Autofleet we support the world’s largest vehicle fleet operators and transportation providers to optimize existing operations and seamlessly launch new, dynamic business models - driving efficient operations and maximizing utilization.

At the heart of our platform lies the data infrastructure, driving advanced machine learning models and optimization algorithms. As the owner of data pipelines, you'll tackle diverse challenges spanning optimization, prediction, modeling, inference, transportation, and mapping.

As a driven Senior Data Engineer, you'll take charge of our processing pipeline, pivotal in optimizing, scaling, expanding data services, and helping with the most impactful problems in transportation.

What You'll Do

• Design and maintain production level Machine Learning and data pipelines.
• Take full data ownership of existing and new features: define data points, extract and manipulate data, and perform validation and testing.
• Create, support, and maintain several data marts, while focusing on data integrity, performance, and long-term maintainability.

• Collaborate with business teams and customers to understand unique needs and requirements.

• 4+ years of backend experience in a data engineering / development role.
• Strong knowledge in Python – a must.
• Proven experience working with SQL and NoSQL databases, data warehouses and data lakes.
• Proven experience of architecting data pipelines - data ingestions, streaming, ETLs, reporting and visualizations.
• Proven record of bootstrapping a data platform or other equivalent large scale data system.
• Experience with spark.
• BSc/BA in computer science or engineering
• Fast learner, independent and with a proven ability to multitask on several projects at a time.
• Ability to identify inefficient processes, and to be proactive in working towards improving them.
• Familiarity with MLops pipelines - a strong advantage
• Familiarity with statistical modeling techniques - an advantage.