Senior Analytics Engineer
About The Position
Our Business team.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. Position Overview We are looking for an experienced and autonomous Analytics Engineer 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. Where you sit in the team The Data function is structured around three layers: Data Management: Ingests raw data from all sources into the Databricks platform. Owns the bronze/raw layer. Analytics Engineering → YOU: Takes raw data, conforms and transforms it, builds KPI definitions and semantic models, and delivers reliable data products to the presentation layer. Analytics & Insights: Consumes the analytics-ready data to produce dashboards, reports and strategic insights for the business.
Requirements
- 5+ years of experience in analytics engineering, data engineering, or a closely related data role.
- Strong, hands-on proficiency with dbt (dbt Core or dbt Cloud) — this is a core requirement.
- Experience working on Databricks (or a comparable cloud data platform such as Snowflake or BigQuery).
- Solid understanding of dimensional modelling, data vault, or similar data warehousing patterns.
- SQL excellence: complex transformations, window functions, query optimisation.
- Proven ability to work autonomously across multiple data domains simultaneously, figuring out unfamiliar data with limited documentation.
- Strong analytical mindset: able to interrogate data critically, spot anomalies, and validate logic end-to-end.
- Excellent communication skills — comfortable talking directly with business stakeholders to elicit requirements and explain data concepts.
- Experience working across diverse data domains (e.g. marketing, product analytics, customer care, financial/subscription data).
Nice To Haves
- Familiarity with SaaS GTM tooling and metrics — e.g. Amplitude, HubSpot, or similar marketing and product analytics platforms.
- Experience with Python for data transformation or pipeline orchestration.
- Exposure to BI/visualisation layers (e.g. Looker, Tableau, Power BI) and how they consume semantic models.
- Experience in a multi-brand or portfolio company context.
- Background in a consulting or contractor capacity: comfortable delivering impact quickly in complex environments.
Responsibilities
- Design and build robust dbt models on Databricks that transform raw, ingested data into clean, conformed, and analytics-ready datasets.
- Define and implement KPI logic in collaboration with business and analytics stakeholders, ensuring consistent definitions across domains.
- Maintain and evolve the semantic/presentation layer, ensuring data products are reliable, tested, documented, and performant.
- Apply software engineering best practices to analytics code: version control, testing, CI/CD, and documentation.
- Independently 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.
- Proactively engage business partners and domain owners to understand context, validate assumptions, and align on KPI definitions.
- Identify data quality issues early and work with the Data Management team to resolve them at source.
- Act as the connective tissue between data engineers and analysts: translating analytical needs into engineering tasks, and surfacing data realities back to the business.
- Work with the Analytics team to ensure the presentation layer meets reporting and self-service needs.
- Contribute to data governance: naming conventions, lineage documentation, and model cataloguing.
- Support the broader team in extending analytics coverage to new brands and domains over time.
Benefits
- Diversity & Inclusion are at our core.
- Respect, openness, and trusted collaboration are valued.
- Commitment to caring for the environment and each other.
- Ongoing ESG efforts and ambitious sustainability goals.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
