Wholesale Credit Risk Associate - Product Strategy

Build robust data foundations that power analytics and intelligent applications. You will develop scalable pipelines and consistent datasets across platforms, turning complex data into clear insights and visualizations. You will own delivery end-to-end across the software development life cycle, from requirements through support. You will partner closely with stakeholders, perform user acceptance testing, and deliver demos using SQL and Python. As a Data/AI Engineer, you will develop robust frameworks to extract data from multiple sources, apply business rules, and deliver comprehensive datasets for end-user consumption. You will ensure cross-platform data consistency and perform gap analysis using advanced software technologies. You will conduct data exploration, analytics, and visualization to support analysis and exploration of large datasets. You will own all phases of the software development life cycle, including requirements gathering, design, development, testing, implementation, and support, with a focus on data models, metadata, and business transformation strategies. You will perform user acceptance testing and deliver demos to stakeholders using SQL queries and Python scripts. Job responsibilities Develop frameworks to extract data from multiple sources, apply business rules, and deliver comprehensive datasets for end-user consumption. Ensure cross-platform data consistency and perform gap analysis using advanced software technologies. Explore data and deliver analytics and visualizations, including insightful dashboards and reports in Tableau. Own end-to-end software development life cycle activities: requirements gathering, design, development, testing, implementation, and support. Define data models, metadata, and data dictionaries to enable thorough analysis and analytical explorations. Perform user acceptance testing and deliver stakeholder demos using SQL queries and Python scripts. Identify hidden data problems and patterns and drive improvements in coding hygiene and system architecture. Build and optimize scalable data pipelines, leveraging tools such as Databricks and PySpark for processing and analysis. Design and build end-to-end generative AI pipelines, including ingestion, preprocessing, model integration, and deployment workflows for large language model-powered applications. Incorporate prompt engineering and vector storage to enable intelligent, context-aware retrieval and response generation. Lead development activities by conducting regular stakeholder meetings, communicating timelines and blockers, and providing guidance to junior engineers and technologists. Required qualifications, capabilities, and skills Education/experience: Master’s degree in Computer Science, Information Technology, Data Engineering, or related field; or Bachelor’s degree in the same fields plus 2 years of experience in the job offered or a similar technical role. Python proficiency, including Pandas and NumPy, with demonstrated experience developing and optimizing extract, transform, load (ETL) pipelines for data migration and processing. SQL proficiency, including window functions and creating stored procedures. Experience with Tableau to create dashboards and reports for analytics and visualization. Strong grounding in data engineering concepts, including ETL processes, data pipeline optimization, and data migration strategies. Experience with Oracle PL/SQL and Microsoft SQL Server for data management and analysis. Experience with AWS, Databricks, and PySpark for data processing and analysis. Experience building and optimizing end-to-end generative AI pipelines, including data preprocessing, large language model integration, prompt engineering, and deployment workflows using LangChain, LlamaIndex, or Hugging Face Transformers. Experience with retrieval-augmented generation architecture, including vector databases (Pinecone, ChromaDB, FAISS) and embedding models for context-aware retrieval systems. Experience leading development activities, conducting stakeholder meetings, and communicating timelines and blockers. Preferred qualifications, capabilities, and skills Experience performing statistical analysis to interpret data, identify trends, and draw conclusions using scikit-learn and TensorFlow. Experience designing and managing scalable data pipelines using Airflow, Linux, and Git. Experience improving pipeline performance using parallelism, multi-threading, and batch processing techniques.