Position Overview
We’re looking for a talented Data Engineer with strong AWS expertise to design, build, and maintain the data infrastructure that powers our vehicle inspection platform. At Pave.ai, you’ll be responsible for developing scalable and reliable data pipelines that process millions of vehicle inspections, images, and automotive data points — delivering real-time insights to customers across the automotive ecosystem.
In this role, you will collaborate closely with our engineering and data science teams based in both Canada and Vietnam, working together to design end-to-end solutions that support advanced analytics, machine learning models, and business intelligence tools. You’ll play a key role in ensuring data accuracy, scalability, and system performance.
Key Responsibilities
Data Pipeline Development
Design and implement scalable ETL/ELT pipelines for processing vehicle inspection data, images, and metadata
Build real-time data processing workflows for instant inspection results and damage detection
Create data ingestion solutions from mobile apps, APIs, IoT devices, and third-party automotive systems
Implement data quality frameworks to ensure inspection accuracy and compliance
Optimize pipelines for processing high-volume image data and computer vision outputs
AWS Data Platform Management
Architect data warehousing solutions using Amazon Redshift for vehicle inspection analytics
Design schemas optimized for automotive data (VIN, inspection history, damage reports, pricing)
Implement data lakes using S3 for storing inspection images, videos, and unstructured data
Manage inspection metadata and vehicle catalogs using AWS Glue Data Catalog
Build ML-ready datasets for computer vision and damage detection models
Analytics \& Visualization
Develop QuickSight dashboards for vehicle inspection metrics, damage trends, and pricing analytics
Create self-service analytics for dealerships, insurers, and fleet operators
Build real-time inspection monitoring dashboards for quality assurance
Implement predictive analytics for vehicle valuation and damage assessment
Design automated reports for inspection volumes, accuracy rates, and customer KPIs
Data Integration \& Orchestration
Integrate with automotive data providers (Carfax, KBB, automotive APIs)
Build real-time processing for mobile inspection data using Kinesis
Implement workflows connecting inspection data with customer CRMs and dealer management systems
Design event-driven architectures for inspection status updates and notifications
Create APIs for inspection data access by partners and third-party platforms
Infrastructure \& Operations
Implement Infrastructure as Code using CloudFormation or Terraform
Set up monitoring and alerting using CloudWatch and SNS
Ensure data security through encryption, VPC configuration, and IAM policies
Optimize AWS costs through resource management and Reserved Instances
Maintain data recovery and backup strategies
Own operational reliability of the data platform, including versioned pipelines, CI/CD integration for test data provisioning, and improvements in data quality and governance to prevent application failures from raw vs. processed data mismatches
Required Qualifications
Experience
4+ years of experience as a Data Engineer or similar role
3+ years of hands-on experience with AWS data services
Experience with image/video data processing and storage at scale
Background in automotive, insurance, or inspection technology is a plus
Proven track record of building production data pipelines for high-volume consumer applications
Technical Skills
AWS Services Expertise:
Amazon Redshift: Cluster management, performance tuning, Spectrum
Amazon QuickSight: Dashboard development, SPICE, ML insights
AWS Glue: ETL jobs, crawlers, data catalog
Amazon S3: Data lake architecture, lifecycle policies, partitioning
Amazon Athena: Query optimization, partition projection
Amazon Kinesis: Real-time data streaming and analytics
AWS Lambda: Serverless data processing
Amazon EMR: Big data processing with Spark/Hadoop
Programming and Tools:
Strong programming skills in Python and SQL
Experience with PySpark or Spark SQL
Proficiency with Git and CI/CD pipelines
Knowledge of data orchestration tools (Airflow, Step Functions)
Familiarity with dbt (data build tool) for data transformation
Data Engineering Concepts:
Strong understanding of data warehousing and data lake architectures
Experience with both batch and stream processing paradigms
Knowledge of data modeling techniques (star schema, data vault)
Understanding of data governance and lineage
Core Competencies
Strong analytical and problem-solving skills
Excellent communication skills for working with technical and business stakeholders
Self-motivated with ability to work independently
Detail-oriented approach to data quality
Passion for automation and optimization
Preferred Qualifications
AWS Certifications (Solutions Architect, Data Analytics Specialty)
Experience with computer vision data pipelines and ML model deployment
Knowledge of automotive industry data standards (VIN decoding, OBD-II)
Experience with geospatial data and location-based analytics
Familiarity with image optimization and CDN strategies
Understanding of data privacy regulations (GDPR, CCPA) for consumer data
Experience with mobile app analytics and real-time data synchronization
Background in building multi-tenant SaaS data architectures
What We Offer
Competitive salary
Flexible work arrangements, including hybrid options
13th-month bonus in accordance with company policy
Comprehensive health, dental, and vision insurance for the employee and one dependent
Professional development budget
Opportunity to shape the future of AI technology
Collaborative and innovative work environment