Analytics Engineer

Role overview

Responsibilities

• Design, build, and maintain scalable, reusable, documented models for analytics, BI, product, and data science use cases.
• Transform raw and intermediate data into curated datasets; apply consistent business logic and develop reusable semantic layers, metrics, and core entities.
• Work with Data Engineering on source structures and transformations that support high-quality analytics outputs.
• Build automated data checks and testing standards for freshness, completeness, consistency, accuracy, schemas, business rules, and metrics.
• Document datasets and definitions and improve discoverability and usability of data assets.
• Partner with Analytics, Product, Data Science, and commercial teams to translate requirements into models and align definitions, KPIs, and business logic.
• Identify opportunities to improve insight generation, consistency, and speed to value.

What you'll need

• Previous experience in an Analytics Engineering position and ability to design and maintain scalable, documented data models.
• Data curation tools such as dbt or Python; complex, efficient SQL, especially on cloud data platforms such as Snowflake.
• Proficiency with orchestration tools such as Airflow; experience with Git and CI/CD and appreciation of FinOps.
• Proficiency with cloud services, ideally AWS.
• Understanding of Agile methodologies and experience using Jira.
• Proactive problem-solving, attention to detail, organization, and ability to work independently or as part of a team.
• Ability to explain technical concepts to business users and learn new skills and technologies quickly.

Application timing

• Applications are accepted until 9 October 2026.

Workplace approach

• Global describes its workforce approach as Smart Working, with a flexible and agile workforce; no specific office or remote schedule is stated.

About Global