Optimize supply chains, predict vehicle parts wear, and build connected car platforms.
Modern automotive systems rely on complex connected architectures. Integrating IoT sensor collection, automated part lifecycle monitoring, and high-performance serverless telemetry storage keeps fleets operational and optimizes assembly pipelines.
Autonomous routing agents that reschedule deliveries and select carriers based on weather, congestion, and cost.
Time-series predictive models estimating battery health decay in electric vehicle fleets.
Connected car telemetry dashboards monitoring fuel efficiency, driver habits, and diagnostic codes.
Robotic assembly line cost models, supply chain risk calculations, and plant overhead analytics.
Secure cryptographic handshakes for vehicle control modules and click-to-start authorization APIs.
High-throughput telemetry ingestion pipelines built using AWS Kinesis, Lambda functions, and DynamoDB.
Practice with 12 structured tasks categorized by difficulty.
Design a SQL schema tracking vehicle registrations, locations, and active diagnostics.
Write a script scrubbing GPS coordinate data from fleet files to ensure driver privacy.
Build a script prompting users for mechanical warning codes to suggest default repairs.
Configure an S3 bucket with lifecycle policies to archive connected car telemetry logs.
Validate incoming vehicle status logs to block incorrect inputs or injection attempts.
Core Skills
Core Skills
Core Skills
