Netradyne
Fleet safety solutions
SeniorDataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Engineer at Netradyne. Skills: Data pipelines, Real-time AI and ML systems, High-throughput streaming pipelines, Production-grade data systems, AWS services, Docker, Kubernetes. Build and scale data platforms that power real-time AI and ML systems. Design and operate high-throughput streaming pipelines”
Industry & Context.
Good problem-solving ability
What They're Looking For.
Must Have
Bachelor’s degree in computer science or related field, 3–6 years of experience in Python-based data backend engineering, programming fundamentals — data structures, algorithms, and OOP, Experience with AWS services (Kinesis, SQS, Lambda, or similar), Experience building and managing data pipelines and workflows (Airflow/DAGs), Good understanding of data modelling and databases (RDBMS/NoSQL), Experience with Docker and Kubernetes, Hands-on experience with GitHub and CI/CD tools like Jenkins, Linux fundamentals and debugging skills, Good problem-solving ability and attention to detail
Nice to Have
Prior experience in working with Product or SaaS Companies, Experience with streaming/event-driven architectures, Exposure to NoSQL databases (MongoDB, Cassandra), Basic understanding of ML pipelines and concepts, Familiarity with modern AI concepts (RAG, AI agents, prompt engineering), Experience in high-scale, real-time data environments, Familiarity with Agile development methodologies
What You'll Do.
Build and scale data platforms that power real-time AI and ML systems
Design and operate high-throughput streaming pipelines
Design and operate production-grade data systems deployed at large scale
Build and maintain scalable data pipelines for real-time and batch processing
Develop data platforms supporting ML workflows including feature generation
and validation pipelines
Optimize pipeline performance
and scalability across high-volume environments
Develop backend components and services for data processing systems
and troubleshoot production data systems
Work with distributed systems in containerized environments
Follow engineering best practices including code quality
Work with AI agents to solve data engineering problems
How You'll Work.
Team & Collaboration
Collaborate with Data Science and ML teams to productionise AI solutions
Full Job Description
Netradyne harnesses the power of Computer Vision and Edge Computing to revolutionize the modern-day transportation ecosystem. We are a leader in fleet safety solutions. With growth exceeding 4x year over year, our solution is quickly being recognized as a significant disruptive technology. Our team is growing, and we need forward-thinking, uncompromising, competitive team members to continue to facilitate our growth. About Netradyne: Netradyne provides AI-powered technologies for fleet management and safer roads. An award-winning industry leader in fleet safety and video telematics solutions, Netradyne empowers thousands of commercial fleet customers across North America, Europe, and Asia to enhance their driver performance, reduce risk, and optimize operations. Netradyne sets the standard among transportation technology companies for enhancing and sustaining road safety, with an industry-leading 25+ billion miles vision-analyzed for risk and an industry-first driver scoring system that reinforces safe behaviors. Founded in 2015, Netradyne is headquartered in San Diego with offices in San Francisco, Nashville, the UK and Bangalore. For more details visit: www.netradyne.com Job Overview: Job Overview: As a Senior Data Engineer at Netradyne, you will build and scale data platforms that power real-time AI and ML systems at the core of our fleet safety products. You will design and operate high-throughput streaming pipelines and production-grade data systems deployed at large scale, working closely with Data Science and ML teams to bring AI solutions to life in a fast-paced, impact-driven environment. Key Responsibilities: Build and maintain scalable data pipelines for real-time and batch processing (Kinesis, SQS-based systems). Develop data platforms supporting ML workflows including feature generation, training, and validation pipelines. Optimize pipeline performance, reliability, and scalability across high-volume environments. Develop backend components and services
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