Analytics Engineer
h3Our Business /h3 pteam.blue is an ecosystem of successful brands working together across regions to provide customers with everything they need to succeed online. 60+ successful brands make up the group, with a team of more than 3,000 experts serving 3.5 million customers across Europe and beyond. /p h3Our Business /h3 pteam.blue is an ecosystem of successful brands working together across regions to provide customers with everything they need to succeed online. 60+ successful brands make up the group, with a team of more than 3,000 experts serving 3.5 million customers across Europe and beyond. team.blue’s brands span traditional hosting businesses — offering domain names, email, shared hosting, e-commerce and server solutions — as well as specialist SaaS providers offering compliance, marketing tools and team collaboration products. This broad portfolio makes team.blue a one-stop partner for online businesses and entrepreneurs across Europe. /p h3Position Overview /h3 pWe are looking for an experienced and autonomous bAnalytics Engineer /b to join our AI Data function. This role sits at the critical intersection between our Data Management team (responsible for ingesting raw data into our Databricks-based data platform) and our Analytics team (responsible for insights and reporting). You will take raw data, at various stages of conforming, and transform it into trusted, analytics-ready datasets — building KPIs, semantic models, and data products that feed our presentation and reporting layers. Working across a wide variety of data domains (marketing, product usage, customer care, revenue, and more), you will need to be intellectually curious, self-directed, and proactive: comfortable exploring unfamiliar data landscapes with limited guidance, engaging business partners to understand what the data really means,
and making sound judgement calls independently. We need someone we can trust to pick up a new data domain, figure it out, and deliver at pace. Reporting to the Director of Analytics, you will work closely with data engineers, analytics engineers, and business stakeholders across the group. /p h3Where you sit in the team /h3 h3The Data function is structured around three layers: /h3 ul liData Management: Ingests raw data from all sources into the Databricks platform. Owns the bronze/raw layer. /li liAnalytics Engineering → YOU: Takes raw data, conforms and transforms it, builds KPI definitions and semantic models, and delivers reliable data products to the presentation layer. /li liAnalytics Insights: Consumes the analytics-ready data to produce dashboards, reports and strategic insights for the business. /li /ul h3Key
responsibilities:
/h3 h3Data Transformation Modelling /h3 ul liDesign and build robust dbt models on Databricks that transform raw, ingested data into clean, conformed, and analytics-ready datasets. /li liDefine and implement KPI logic in collaboration with business and analytics stakeholders, ensuring consistent definitions across domains. /li liMaintain and evolve the semantic/presentation layer, ensuring data products are reliable, tested, documented, and performant. /li liApply software engineering best practices to analytics code: version control, testing,
CI/CD, and documentation. /li /ul h3Cross-Domain Data Exploration /h3 ul liIndependently onboard new data domains (e.g. marketing attribution, product usage, customer care, subscription data) with limited guidance — exploring the data, understanding its structure and meaning, and deciding how to best model it. /li liProactively engage business partners and domain owners to understand context, validate assumptions, and align on KPI definitions. /li liIdentify data quality issues early and work with the Data Management team to resolve them at source. /li /ul h3Collaboration Enablement /h3 ul liAct as the connective tissue between data engineers and analysts: translating analytical needs into engineering tasks, and surfacing data realities back to the business. /li liWork with the Analytics team to ensure the presentation layer meets reporting and self-service needs. /li liContribute to data governance: naming conventions, lineage documentation, and model cataloguing. /li liSupport the broader team in extending analytics coverage to new brands and domains over time. /li /ul h3Required Experience/skills: /h3 ul li5+ years of experience in analytics engineering, data engineering, or a closely related data role. /li liStrong, hands‑on proficiency with dbt (dbt Core or dbt Cloud) — this is a core requirement. /li liExperience working on Databricks (or a comparable cloud data platform such as Snowflake or BigQuery). /li liSolid understanding of dimensional modelling, data vault, or similar data warehousing patterns. /li liSQL excellence: complex transformations, window functions, query optimisation. /li liProven ability to work autonomously across multiple data domains simultaneously, figuring out unfamiliar data with limited
and making sound judgement calls independently. We need someone we can trust to pick up a new data domain, figure it out, and deliver at pace. Reporting to the Director of Analytics, you will work closely with data engineers, analytics engineers, and business stakeholders across the group. /p h3Where you sit in the team /h3 h3The Data function is structured around three layers: /h3 ul liData Management: Ingests raw data from all sources into the Databricks platform. Owns the bronze/raw layer. /li liAnalytics Engineering → YOU: Takes raw data, conforms and transforms it, builds KPI definitions and semantic models, and delivers reliable data products to the presentation layer. /li liAnalytics Insights: Consumes the analytics-ready data to produce dashboards, reports and strategic insights for the business. /li /ul h3Key
responsibilities:
/h3 h3Data Transformation Modelling /h3 ul liDesign and build robust dbt models on Databricks that transform raw, ingested data into clean, conformed, and analytics-ready datasets. /li liDefine and implement KPI logic in collaboration with business and analytics stakeholders, ensuring consistent definitions across domains. /li liMaintain and evolve the semantic/presentation layer, ensuring data products are reliable, tested, documented, and performant. /li liApply software engineering best practices to analytics code: version control, testing,
CI/CD, and documentation. /li /ul h3Cross-Domain Data Exploration /h3 ul liIndependently onboard new data domains (e.g. marketing attribution, product usage, customer care, subscription data) with limited guidance — exploring the data, understanding its structure and meaning, and deciding how to best model it. /li liProactively engage business partners and domain owners to understand context, validate assumptions, and align on KPI definitions. /li liIdentify data quality issues early and work with the Data Management team to resolve them at source. /li /ul h3Collaboration Enablement /h3 ul liAct as the connective tissue between data engineers and analysts: translating analytical needs into engineering tasks, and surfacing data realities back to the business. /li liWork with the Analytics team to ensure the presentation layer meets reporting and self-service needs. /li liContribute to data governance: naming conventions, lineage documentation, and model cataloguing. /li liSupport the broader team in extending analytics coverage to new brands and domains over time. /li /ul h3Required Experience/skills: /h3 ul li5+ years of experience in analytics engineering, data engineering, or a closely related data role. /li liStrong, hands‑on proficiency with dbt (dbt Core or dbt Cloud) — this is a core requirement. /li liExperience working on Databricks (or a comparable cloud data platform such as Snowflake or BigQuery). /li liSolid understanding of dimensional modelling, data vault, or similar data warehousing patterns. /li liSQL excellence: complex transformations, window functions, query optimisation. /li liProven ability to work autonomously across multiple data domains simultaneously, figuring out unfamiliar data with limited