Data Engineer- Manager

KPMG is currently seeking a Data Engineer to join our Audit Technology Alliance organization.

• Take ownership of designing and implementing data pipelines focused on context engineering, transforming vast amounts of structured(e.g., transactional) and unstructured client data into high-quality inputs for our AI solutions
• Set the standard for data engineering excellence,establishing and evangelizing best practices for processing and modeling diverse data sets to be analyzed by advanced generative AI agents
• Serve as a key subject matter expert, collaborating closely with AI and solution architecture teams to define and deliver the specific data schemas and contextual payloads required by our AI orchestration frameworks
• Support the development of Retrieval-Augmented Generation (RAG) and context engineering pipelines from audit knowledge sources and the integration into AI agent workflows; design and implement the use of metadata across knowledge systems to drive the use of context
• Champion data quality by developing and implementing rigorous validation frameworks to ensure the reliability of all data sources, which is critical for generating verifiable AI outputs
• Drive the product forward by personally prototyping and evolving our data engineering strategies, pioneering innovative techniques to handle complex data relationships andkeepour context engineering capabilities state-of-the-art
• Act with integrity, professionalism, and personal responsibility to uphold KPMG's respectful and courteous work environment

• Minimum fiveyears of recent professional experience in data engineering, with a proven track recordof building data solutions that support AI-driven or advanced analytics systems
• Bachelor’s degree from an accredited college or university;master’s degree from an accredited college or university in Computer Science, Engineering, Information Systems, or a related field is preferred
• Deepexpertisein designing and building data pipelines that process both structured and unstructured data, preparing it for advanced AI consumption using cloud platforms like Databricks and Microsoft Fabric
• Demonstrated experience in data preparation and structuring for Retrieval-Augmented Generation (RAG) systems, with hands-on knowledge of tools like Azure AI Search and a strong understanding of how data integrates into AI agent workflows
• Strong, independent programming skills in Python and significant experience with microservice architecture, including building data APIs and interacting with asynchronous messaging systems
• Proficiency with Agile methodologies and SDLC tools (Azure Dev Ops, Git, CI/CD) combined with excellent problem-solving skills to articulate complex data concepts and influence technical direction (200
  
  TEC)