Data Product Engineer, Data & Analytics

Drive Innovation as our Next 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.

What Makes This Role Interesting:

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.

Your Key Responsibilities:

Build & scale data and analytics engineering

• 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.

Expand AI-native practices & solution delivery

• 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.

Apply product judgment & engage stakeholders

• 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 & guide the team

• 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 developments in data, software engineering, and AI tooling, helping the team adopt practices that simplify delivery and improve outcomes.

Your Essential Skills & Experience:

• 5+ years of experience in data engineering, analytics engineering, software engineering, or an adjacent field.
• Demonstrate mastery of data engineering and analytics engineering, including data modeling and ELT design, pipeline performance, reliability, and maintainability. Be an opinionated systems thinker.
• Deep practical experience with SQL, Python, data modelling, ELT, dbt, orchestration, and cloud data services—preferably using GCP (BigQuery), dbt, Airflow.
• Strong DevOps practices: Git, code review, automated testing, CI/CD, x as code, environment management, observability.
• Practical experience designing workflows for and directing AI coding agents, and validating generated designs, code, tests, and documentation.
• Experience building and maintaining production APIs, MCP servers, and endpoints.
• Ability to communicate technical decisions and trade-offs to technical and non-technical stakeholders.
• The ideal candidate is a pragmatic, self-directed builder who …