Lead Analytics Engineer

Let's introduce ourselves

By joining the Platform Data Spoke, you will be part of a new team with a clear vision: to leverage data, analytics, and AI to guide decisions, amplify value, and unlock new opportunities that extend and fuel sustainable growth. The spoke plays a strategic role in enabling leadership and teams to make smarter decisions and innovate faster. You will work hands-on with a modern technology stack while shaping best practices for how platform and cross-functional data is used. This is an opportunity to grow your skills in a truly international environment, where collaboration, learning, and trust are at the core of how we work.

What you'll do

The LeadAnalytics Engineer is the technical anchor of the Platform Data Spoke. You will shape and deliver high-quality data products that are trusted, reusable, and cost-efficient. By bridging engineering and analytics, you ensure platform data can be integrated across functions and easily consumed by others. You’ll also set standards, mentor the team, and guide the spoke’s technical foundation.

• Design, model, and document data products that provide trusted and reusable datasets.
• Build and optimize pipelines to support both batch and streaming data products.
• Collaborate with other functions to integrate cross-company data into the platform.
• Ensure data products are discoverable, governed, and consumable by multiple stakeholders.
• Drive adoption of DataOps practices: testing, observability, version control.
• Act as a technical reference point for insight and dashboard data products.
• Mentor team members and establish engineering standards for data product development

What you'll bring

You see data not just as pipelines or dashboards, but as data products that should be trusted, reusable, and valuable for the whole company. You are curious and motivated by understanding how data connects to business impact, and you thrive in a collaborative environment where engineers, analysts, and stakeholders solve problems together — across countries and cultures.

• Typically 7+ years of experience in data engineering, analytics engineering, or similar roles 
• Strong SQL and data modeling skills; proven experience with cloud data warehouses.
• Hands-on with modern data product tooling such as dbt, Airflow, Databricks, or similar orchestration/transformation frameworks.
• GitOps practices: GitHub, declarative configs (YAML), CI/CD.
• Experience with data quality, monitoring, and product lifecycle management.
• Track record of collaboration across technical and business teams.
• Leadership ability: mentoring, setting standards, guiding design choices.
• Comfortable working in a global, distributed team setup.