Key Responsibilities Requirement Analysis: Collaborate with stakeholders to understand businessrequirements and data sources, and define the architecture and design of dataengineering models to meet these requirements. Architecture Design: Design scalable, reliable, and efficient data engineering models,including algorithms, data pipelines, and data processing systems, to support businessrequirements and quantitative analysis. Technology Selection: Evaluate using POCs and recommend appropriate technologies,frameworks, and tools for building and managing data engineering models, consideringfactors like performance, scalability, and cost-effectiveness. Data Processing: Develop and implement data processing logic, including data cleansing,transformation, and aggregation, using technologies such as AWS Glue, Batch, Lambda. Quantitative Analysis: Collaborate with data scientists and analysts to develop algorithmsand models for quantitative analysis, using techniques such as regression analysis,clustering, and predictive modeling. Model Evaluation: Evaluate the performance of data engineering models using metricsand validation techniques, and iterate on models to improve their accuracy andeffectiveness. Data Visualization: Create visualizations of data and model outputs to communicateinsights and findings to stakeholders.Data Engineering: Understanding of Data engineering principles and practices, includingdata ingestion, processing, transformation, and storage, using tools and technologiessuch as AWS Glue, Batch, Lambda. Quantitative Analysis: Proficiency in quantitative analysis techniques, including statisticalmodeling, machine learning, and data mining, with experience in implementingalgorithms for regression analysis, clustering, classification, and predictive modeling. Programming Languages: Proficiency in programming languages commonly used in dataengineering and quantitative analysis, such as Python, R, Java, or Scala,