Data Product Engineer, Data & Analytics

Ready to participate in shaping the next evolution of our central Data & Analytics team as we elevate our reporting and data-product foundation for the agentic AI era? The central Data & Analytics team delivers trusted, governed data products for company-wide reporting through data engineering and analytics practices. In partnership with the Data Platform Team, we power the company’s data backbone and help ensure data supports day-to-day operations and decision-making.

To improve human and agentic AI data consumption, we are enhancing context and metadata management, extending our semantic layer, and strengthening deployment and AI-assisted workflows so these services remain governed, scalable, and resilient.

This is a hands-on opportunity to shape the team’s future. Working alongside experienced colleagues, you will influence direction and build solutions that turn promising ideas into durable data products, capabilities, and services.

• Design, build, and improve reliable data pipelines, analytical models, and reusable data products for BI, internal applications, and AI consumers.
• Provide opinionated technical leadership and establish standards for architecture, modeling, orchestration, testing, data contracts, documentation, ownership, access control, and lifecycle management.
• Shape and operate a governed semantic layer that provides consistent metrics and business concepts across reporting, applications, APIs, and AI-enabled services.
• Improve operational excellence through data-quality controls, observability, monitoring, alerting, incident practices, performance optimization, CI/CD, and cost management.

• Evolve the team’s AI-native practices, including how it defines work for (coding) agents and reviews generated designs, code, tests, and documentation.
• Make architectural decisions, dependencies, operational knowledge, and business context accessible and reusable by colleagues and AI agents.
• Deliver solutions end to end—from discovery and requirements through implementation, deployment, operation, and iteration—including internal applications, APIs, automation, and governed agent interfaces such as MCP servers.
• Improve shared workflows for programmatic dashboard deployment, authentication and authorization, access management, onboarding, and self-service, partnering with platform and security specialists where appropriate.

• Anticipate internal customer needs through active listening and research, translating insights into priorities for the team’s product and service roadmap.
• Apply product judgment to determine which problems to solve, what a good solution looks like, and how to balance user value with security, maintainability, and total cost of ownership.
• Help users discover and consume data products and semantic models, clearly communicating definitions, ownership, freshness, quality, limitations, and appropriate validation.
• Define explicit data and service contracts with dependent teams, including ownership, interfaces, service expectations, versioning, and escalation paths.

• Mentor colleagues through pairing, technical guidance, and thoughtful reviews across data engineering, analytics engineering, and solution delivery.
• Help the team consistently apply agreed standards for modeling, testing, code review, deployment, observability, documentation, and governance.
• Support the adoption of AI-native practices through practical guidance, review approaches, documentation, and reusable templates.
• Evaluate relevant…