AI and Data Engineer

Key Responsibilities:

Data Acquisition and Management:

• Obtain and manage real-world data from diverse telematics sources.

• Ensure data includes relevant metrics for analysis across different applications.

• Integrate telematics data with other relevant data sources and systems.

• Implement AI-driven data validation and anomaly detection techniques to maintain data integrity.

Data Processing and Integration:

• Develop and maintain scalable data pipelines for collecting, processing, and aggregating large volumes of telematics data.

• Ensure data quality, consistency, and compliance with relevant regulations.

• Collaborate with various specialists to integrate different types of data (e.g., geospatial, time-series, event data).

• Utilize machine learning algorithms to enhance data processing and integration efficiency.

System Design and Optimization:

• Design and implement efficient data storage solutions for high-volume, high-velocity data.

• Optimize data retrieval and query performance for analytics and reporting.

• Develop APIs and interfaces for data access and integration with other systems.

• Leverage AI techniques to optimize data storage and retrieval processes.

Collaboration and Reporting:

• Work closely with data scientists, domain experts, and other team members.

• Prepare technical documentation and presentations describing data architectures and methodologies.

• Contribute to project reports and stakeholder communications.

• Develop AI-driven dashboards and reporting tools to provide actionable insights to stakeholders.

Additional Responsibilities:

• Perform relevant job duties as required by specific projects.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.

• Proven experience in data engineering, particularly with telematics or similar high-volume, real-time data sources.

• Strong skills in SQL, Python, and data pipeline tools (e.g., Apache Kafka, Airflow, Spark).

• Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and big data technologies.

• Knowledge of data modeling, ETL processes, and database design.

• Excellent problem-solving skills and attention to detail.

• Strong communication skills for cross-functional collaboration and reporting.

Preferred Qualifications:

• Experience with streaming data processing and real-time analytics.

• Familiarity with machine learning pipelines and data preparation for AI applications.

• Knowledge of data privacy regulations and security best practices.

• Experience in one or more domain areas such as transportation, smart cities, or industrial applications.

References: Please provide details of a recent relevant project (within the last five years) including:

• A short description of the work

• The dates of work performance

• The name of the client

• A contact phone number and email address for reference check