Data Engineer

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

Required

* 5+ years in data engineering, analytics engineering, software engineering, or a closely related technical role.

* Hands-on experience building production data pipelines using SQL, Python, Spark, orchestration tools, or similar technologies.

* Strong understanding of data modeling, data quality, data integration, and enterprise data lifecycle practices.

Experience working with cloud data platforms, data lakes, data warehouses, application programming interfaces, and enterprise source systems.

* Ability to work with both structured data and unstructured knowledge sources used by AI agents and retrieval-based solutions.

* Clear communicator who can explain data issues, technical tradeoffs, and delivery risks to business and leadership audiences.

Preferred

* Experience supporting AI, machine learning, retrieval, knowledge indexing, vector search, or agent-based use cases.

* Experience with Microsoft data and AI platforms such as Azure, Microsoft Fabric, Azure OpenAI, Copilot Studio, Power Platform, or Microsoft 365.

* Familiarity with ServiceNow, enterprise workflow systems, customer support data, services data, or operational data environments.

* Background in technical consulting, data architecture, analytics enablement, or business-facing engineering.

* Experience documenting reusable patterns, data contracts, and operating practices that can scale across teams.

What Success Looks Like

* AI squads have reliable data foundations in place at the end of each sprint, not just one-off extracts or manual workarounds.

* Data products are trusted, documented, secure, and reusable across squads and functions.

* AI agents, automation, and analytics perform better because they are grounded in clean, relevant, and accessible data.

* Business teams can see measurable impact through faster decisions, reduced manual work, and improved process visibility.

* Platform and data teams can operate and evolve the pipelines after the engagement ends.