The ideal candidate combines strong Data Engineering fundamentals with the ability to work closely with business stakeholders, develop reporting solutions, and contribute across multiple data-related initiatives. Exposure to Data Science and AI use cases is considered an advantage.
Assist in developing AI solution architectures, HLD/LLD, data-flow designs, sizing, BoM, costing, implementation roadmaps and proposals for RFI/RFP/tenders.
Interest in or experience developing and executing automated tests, investigate and assist in problem analysis, and resolution of delivered software components
Building business cases and ROI models that link digital initiatives to measurable outcomes such as asset performance, production efficiency, reliability, energy optimization, sustainability, safety, and workforce productivity.
Building business cases and ROI models that link digital initiatives to measurable outcomes such as asset performance, production efficiency, reliability, energy optimization, sustainability, safety, and workforce productivity.
Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.