Data Product Engineer, Data & Analytics

• Design, build, and improve reliable data pipelines, analytical models, and reusable data products for BI, internal applications, and AI consumers
• Establish standards for architecture, modeling, orchestration, testing, data contracts, documentation, ownership, access control, and lifecycle management
• Shape and operate a governed semantic layer for consistent metrics and business concepts
• Improve data-quality controls, observability, monitoring, alerting, incident practices, performance, CI/CD, and cost management
• Evolve AI-native practices for coding agents and review generated designs, code, tests, and documentation
• Make architectural decisions, dependencies, operational knowledge, and business context accessible to colleagues and AI agents
• Deliver internal applications, APIs, automation, and governed agent interfaces such as MCP servers end to end
• Improve workflows for dashboard deployment, authentication, authorization, access management, onboarding, and self-service
• Research internal customer needs and translate insights into product and service roadmap priorities
• Help users discover and consume data products and semantic models, communicating definitions, ownership, freshness, quality, limitations, and validation requirements
• Define data and service contracts with dependent teams
• Mentor colleagues through pairing, technical guidance, and reviews
• Guide adoption of standards for modeling, testing, code review, deployment, observability, documentation, and governance
• Evaluate developments in data, software engineering, and AI tooling and help adopt practices that improve outcomes

• 5+ years of experience in data engineering, analytics engineering, software engineering, or an adjacent field
• Mastery of data engineering and analytics engineering, including data modeling and ELT design, pipeline performance, reliability, and maintainability
• Deep practical experience with SQL, Python, data modelling, ELT, dbt, orchestration, and cloud data services
• Preferably experience using GCP, BigQuery, dbt, and Airflow
• Strong DevOps practices: Git, code review, automated testing, CI/CD, infrastructure as code, environment management, and 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
• Experience with BI and analytics governance, metric consistency, semantic modeling, access control, data quality, discoverability, lifecycle management, and cost management
• Experience implementing semantic layers, metric stores, data catalogs, or governed analytical interfaces
• Experience building internal applications with authentication and role-based access

• Values-driven culture centered on Belong, Grow, and Innovate
• Collaborative and supportive team culture
• Opportunities to build a career and drive collective growth
• Entrepreneurial environment with ownership and initiative
• Purpose-driven work with tangible real-world impact
• Accommodation support during the application process