The Role
As a Data Engineer, you will design and optimize ETL processes and data pipelines for time-series data, ensuring data quality and efficiency while collaborating with Data Scientists on production systems.
Summary Generated by Built In
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
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.
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
- 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.
- 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.
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.
Top Skills
AWS
Databricks
Delta Lake
Pyspark
Python
Spark
SQL
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The Company
What We Do
Questar Auto Technologies is a predictive vehicle health company introducing cutting-edge AI technology for automotive. Our technology consists of AI-based analytics software, advanced telematics, and years of accumulated data and intimate familiarity with automotive pain points. Questar uses unique prediction models that will provide accurate prognosis of future vehicle health events before they affect our customers – automakers, Tier 1 suppliers, and fleets, and help them unlock the value of vehicle data. These deep insights will change the way fleets are managed, optimize expenditure, increase operational efficiency and safety with a clear and aggressive ROI.
