> Markdown version of [/jobs/ext/3971739-data-engineer-manager](https://www.wearedevelopers.com/jobs/ext/3971739-data-engineer-manager). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer- Manager - **Company:** Kpmg LLP - **Location:** Washington, DC, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Microsoft Azure, Information Systems, Computer Programming, Continuous Integration, Data Cleansing, Information Engineering, Data Files, Data Systems, Python (Programming Language), Knowledge-Based Systems, Systems Development Life Cycle, Message Oriented Middleware, Unstructured Data, Enterprise Search, Multi-Agent Systems, Generative AI, Git, Microsoft Fabric, Information Technology, Data Management, Virtual Agents, Data Pipelines, Databricks, Microservices - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/data-engineer-manager-washington-dc--4ff3c06e-0a1e-49b7-b5d0-83c61afd8930 ## About the Role Consulting, Community Support, Customer/Client Research, Data Management, Artificial Intelligence (AI), Unstructured Data, Best Practices, Artificial Intelligence (AI) Agents, Data Sets, Data Modeling, * 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 * Bachelors degree from an accredited college or university; masters 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 DevOps, Git, CI/CD) combined with excellent problem-solving skills to articulate complex data concepts and influence technical direction (200TEC) ## Description Data Engineer- Manager * Why KPMG search arrow - right * Practice Areas search arrow - right * Early Career search arrow - right * Experienced search arrow - right * Contractor search arrow - right * Executive search arrow - right * Job Search search arrow - right * Talent Community * Events * US-KPMG.com, * 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-art * Act with integrity, professionalism, and personal responsibility to uphold KPMGs respectful and courteous work environment, * Careers Home * Early Career * Experienced * Contractor * Executive * Practice Areas * Advisory * Audit and Assurance * Tax * Business Support Services * Innovation & Technology * Why KPMG * Who We Are * Benefits & How We Work * Professional Development * Community Impact * Job Search * US-KPMG.com