ICT Data Engineer

The ideal candidate combines strong analytical skills with practical experience building scalable analytics, models, and data products in enterprise environments. You will be part of a talented team of data scientists, engineers, driving predictive analytics and early detection of emerging warranty trends using vast datasets across the enterprise.

Key Responsibilities:

• Assembling large, complex sets of data that meet non-functional and functional business requirements
• Design, implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
• Develop robust ETL (Extract, Transform, Load) process to integrate data from various sources.
• Identifying, designing and implementing internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
• Building required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS, Azure, DB2 and SQL technologies
• Building scalable tables to provide actionable insight into key business performance metrics including operational efficiency and customer acquisition
• Working with stakeholders including the Data Product teams to support their data infrastructure needs while assisting with data-related technical issues
• Design and maintain data models, schemas, and database structures to support analytical and operational use cases.
• Optimize data storage and retrieval mechanisms for performance and scalability.
• Lead and coordinate cross-functional AI programs from concept to deployment, ensuring alignment with business goals and timelines.
• Collaborate with other data scientists, engineers, and business stakeholders to define and prioritize program objectives.
• Apply statistical analysis and machine learning techniques to solve business and operational problems.
• Partner with business stakeholders to understand requirements and translate them into analytical solutions.
• Translate business needs into actionable AI use cases and technical requirements
• Build and deploy predictive models to forecast warranty claims, failure rates, and cost trends.
• Ensure data quality, lineage, documentation, and compliance with governance requirements
• Create dashboards and analytical outputs that drive insight adoption and operational impact
• Collaborate with business data engineers, and platform teams on scalability, performance, and best practices