Analytics Engineer

Experteer Overview

In this Analytics Engineer role within team.blue’s AI & Data function, you will transform raw data into analytics-ready datasets and data products that power reporting and insights across multiple brands. You’ll bridge the Data Management and Analytics teams, defining KPI logic, maintaining a semantic layer, and delivering reliable data assets at pace. You’ll work across diverse domains—marketing, product usage, customer care, revenue—and engage business partners to ensure data meaning and quality. This is a hands‑on, autonomous role at scale in a Databricks-based environment with strong emphasis on governance and collaboration.Retribuzione / Benefits

• Design and build robust dbt models on Databricks to produce clean, conformed datasets
• Define KPI logic with stakeholders to ensure consistent metrics across domains
• Maintain and evolve the semantic/presentation layer with reliable, tested data products
• Apply software engineering practices to analytics code, including version control, testing, CI/CD, and documentation
• Onboard new data domains independently, exploring structure and modelling decisions
• Engage domain owners to validate context and align on KPI definitions
• Identify data quality issues early and coordinate with Data Management to resolve at the source
• Bridge data engineers and analysts by translating analytical needs into engineering tasks and ensuring the presentation layer meets reporting needs
• Contribute to data governance through naming conventions, lineage, and model cataloguing
• Support extending analytics coverage to new brands and domains over time

Responsabilità

• 5+ years in analytics engineering, data engineering, or related data role
• Strong, hands‑on dbt (dbt Core or dbt Cloud) proficiency
• Experience with Databricks or comparable cloud platforms (Snowflake, BigQuery)
• Solid understanding of dimensional modelling or data warehousing patterns
• SQL excellence with complex transformations and window functions
• Ability to work autonomously across multiple data domains with limited documentation
• Strong analytical mindset to interrogate data and validate logic end‑to‑end
• Excellent communication skills with business stakeholders
• Experience across diverse data domains (marketing, product analytics, customer care, financial/subscription data)

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