Data Engineer

Description and Requirements

• About the Role
• Lenovo Solutions & Services Group is running a company-wide AI transformation program, embedding technical talent directly inside business functions. Data Engineers are the foundation builders in that model, making sure the right data is available, reliable, governed, and ready for AI solutions that solve real business problems.
• As a Data Engineer, you will work side by side with Domain Leaders, Forward Deployed Engineers, subject matter experts, and platform partners to turn business workflows into practical data products. You will build, connect, and maintain the data pipelines, curated datasets, and retrieval-ready knowledge sources that allow AI agents, automation, analytics, and decision tools to work in production.
• The right candidate is energized by messy, real-world enterprise data and knows how to make it usable. You are comfortable moving from discovery to working data pipeline to production-ready data asset within sprint cycles, while balancing speed, data quality, security, and long-term maintainability.

What You Will Do

• Discover and Define
• Embed with business functions to understand workflows, source systems, pain points, and data gaps.
• Translate business needs into clear data requirements, source mappings, quality rules, and delivery plans.
• Build and Operate Data Foundations
• Design, build, and maintain scalable data pipelines that move data from enterprise systems into usable, trusted data products.
• Create curated datasets, semantic layers, and reusable data assets that support AI agents, automation, analytics, and reporting.
• Work with structured and unstructured content, including documents, knowledge bases, operational records, and workflow data.
• Enable AI and Retrieval Use Cases
• Prepare data for AI solutions, including chunking, enrichment, metadata tagging, indexing, and retrieval-based architectures.
• Partner with Forward Deployed Engineers to test data quality, retrieval performance, and end-user relevance in real workflows.
• Help connect data assets into Microsoft AI and productivity platforms, Azure services, internal platforms, and workflow tools where appropriate.
• Drive Data Quality, Security, and Adoption
• Implement validation, monitoring, lineage, and documentation so data products can be trusted and maintained after deployment.
• Work within enterprise standards for privacy, security, access control, and responsible use of data.
• Support adoption by making data products understandable to business users and easy for technical teams to reuse.
• Communicate and Collaborate
• Bridge business stakeholders, platform teams, data teams, and AI builders by explaining tradeoffs in plain language.
• Provide concise updates on progress, risks, blockers, and data dependencies to technical and senior non-technical audiences.

What We're Looking For