Questar Auto Technologies

Questar offers AI-powered insights to prevent vehicle downtime by predicting health issues. It uses deep learning models and telematics to collect and analyze vehicle data for actionable diagnostics. The platform serves automakers, Tier 1 suppliers, and fleets as a B2B solution, leveraging predictive maintenance, vehicle health management, and data analytics. It operates as part of a 360° platform that integrates historical and real-time data to deliver predictive insights. The system is deployed across hundreds of thousands of vehicles in multiple countries, reducing maintenance costs and improving safety.

AIData AnalyticsPredictive MaintenanceTelematicsVehicle Health Managementquestarauto.com

Questar Auto Technologies

AIData AnalyticsPredictive MaintenanceTelematicsVehicle Health Managementquestarauto.com

HQHerzliya

Team Size122

Open Jobs12

Total Funding-

Latest FundraiseUnknown

Join the Team

Data Engineer

HybridHerzliya, Tel Aviv District

Hybrid • Herzliya, Tel Aviv District

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Data Scientist

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Mobile Developer

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Frontend Developer

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Machine Learning Engineer

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DevOps Engineer

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Technical Writer

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Internship • Amsterdam, NL

Questar Auto Technology is seeking a talented Data Engineer to join our AI and analytics organization. We process billions of time-series data points from heavy-duty vehicles and fleets, powering predictive maintenance, vehicle health scoring, and next-generation fleet intelligence.

About the Role

As a Data Engineer at Questar, you will own the architecture and optimization of large-scale ETL processes that transform raw heavy-duty vehicle telemetry into production-grade intelligence. You will operate at the intersection of Big Data and AI, building scalable pipelines, enforcing data quality standards, and managing cost-efficiency for a system processing billions of time-series records. You will be a technical owner, collaborating directly with Data Scientists to ensure our fleet intelligence models run reliably in production.

What You’ll Do

What You Bring

Why Join Us

Work directly with massive datasets and real production systems from day one.
Accelerate your professional development with mentorship from experienced data and ML engineers.
Gain hands-on expertise in Databricks, Spark, AWS, Delta Lake, and scalable data orchestration.
Contribute to a mission that improves safety, reliability, and operational efficiency for fleets.
Be part of a collaborative, innovative team where junior engineers can meaningfully shape solutions.
Competitive compensation and a fast-moving environment that rewards learning and initiative.

Location:

Herzeliya (Hybrid)

Type:

Full-time

Who we are?

Questar Auto Technologies is a company at the forefront of the automotive industry, introducing innovative AI technology for predictive vehicle health & Driver behavior. We help our clients in the automotive sector, including automakers and fleet operators, to unlock the value of their vehicle data and make informed decision through insights delivered to them through our systems. Our solution combines cutting-edge AI-based analytics software, advanced data analytics, and extensive experience in the commercial vehicles industry telematics. By performing deep learning both onboard vehicles and in the cloud, Questar's technology can predict potential issues before they occur, enabling proactive management of maintenance, drivers, and mission, reducing TCO.

Questar system is deployed in 20 countries and some of the largest OEMs & Teir1 are part of the company customers.

Architect and build

robust ETLs and scalable data pipelines on Databricks and AWS.

Optimize

high-throughput ingestion workflows for billions of time-series records, ensuring low latency and data integrity.

Engineer

data validation frameworks and automated monitoring to proactively detect anomalies before they impact models.

Drive cost-efficiency

by tuning Spark jobs and managing compute resources in a high-volume environment.

Transform

raw IoT/telemetry signals into structured, enriched Feature Stores ready for Machine Learning production.

Define

best practices for data engineering, CI/CD for data, and lakehouse architecture across the organization.

Production Experience:

3+ years in Data Engineering with strong proficiency in Python, SQL, and PySpark.

Big Data Architecture:

Proven track record working with distributed processing frameworks (Spark, Delta Lake) and cloud infrastructure (AWS preferred).

Scale:

Experience handling high-volume datasets (TB scale or billions of rows); familiarity with time-series or IoT data is a strong advantage.

Engineering Rigor:

Deep understanding of data structures, orchestration (Databricks Workflows), and software engineering best practices (Git, CI/CD).

Problem Solving:

Ability to diagnose complex performance bottlenecks in distributed systems and implement cost-effective solutions.

Ownership:

A self-starter mindset with the ability to take a vague requirement and deliver a deployed, production-ready pipeline.