Senior Data Engineer at Autofleet

Tel-Aviv, Tel-Aviv District, Israel -

Full Time

Start Date

Immediate

Expiry Date

25 Jul, 26

Salary

0.0

Posted On

26 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Spark, SQL, Google Cloud Platform, Data Pipelines, ETL, ELT, Airflow, Docker, Kubernetes, Data Modeling, Distributed Systems, CI/CD, Data Infrastructure, Machine Learning, Backend Engineering

Industry

Software Development

Description

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 Senior Data Engineer, you will play a key role in owning and scaling the backend data infrastructure that powers our platform—supporting real-time optimization, advanced analytics, and machine learning applications. What You'll Do Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark, and other modern tools. Own the backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets. Leverage Google Cloud Platform (GCP) services to architect and operate scalable, secure, and cost-effective data solutions across the pipeline lifecycle. Develop and optimize ETL/ELT workflows across multiple environments to support internal applications, analytics, and machine learning workflows. Build and maintain data marts and data models with a focus on performance, data quality, and long-term maintainability. Collaborate with cross-functional teams including development teams, product managers, and external stakeholders to understand and translate data requirements into scalable solutions. Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability. 4+ years in backend data engineering or infrastructure-focused software development. Proficient in Python, with experience building production-grade data services. Solid understanding of SQL Proven track record designing and operating scalable, low-latency data pipelines (batch and streaming). Experience building and maintaining data platforms, including lakes, pipelines, and developer tooling. Familiar with orchestration tools like Airflow, and modern CI/CD practices. Comfortable working in cloud-native environments (AWS, GCP), including containerization (e.g., Docker, Kubernetes). Bonus: Experience working with GCP Bonus: Experience with data quality monitoring and alerting Bonus: Strong hands-on experience with Spark for distributed data processing at scale. Degree in Computer Science, Engineering, or related field.

Responsibilities

Design, implement, and maintain scalable data pipelines for batch and real-time processing using modern tools like Spark. Own the backend data infrastructure, including ingestion, transformation, and orchestration of large-scale datasets on Google Cloud Platform.