Data Engineer- Manager

Known for being a great place to work and build a career, KPMG provides audit, tax and advisory services for organizations in today's most important industries. Our growth is driven by delivering real results for our clients. It's also enabled by our culture, which encourages individual development, embraces an inclusive environment, rewards innovative excellence and supports our communities. With qualities like those, it's no wonder we're consistently ranked among the best companies to work for by Fortune Magazine, Consulting Magazine, Seramount, Fair
360 and others. If you're as passionate about your future as we are, join our team.

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

Responsibilities:

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 and keep our context engineering capabilities state-of-the-artAct with integrity, professionalism, and personal responsibility to uphold KPMG's respectful and courteous work environment

Qualifications:

Minimum five years of recent professional experience in data engineering, with a proven track record of 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

Deep expertise in 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)
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