Data Engineer
Pubblicato il 24-10-2026 - Team.Blue in Firenze
Our Business
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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 3000+ experts serving its 3.5 million customers across Europe and beyond.
team.blue's brands are a mix of traditional hosting businesses, offering services from domain names, email, shared hosting, e-commerce and server hosting solutions and specialist SaaS providers offering adjacent products such as compliance, marketing tools and team collaboration products. This broad product offering makes it a one-stop partner for online businesses and entrepreneurs across Europe.
Position Overview
As a Senior Data Platform Engineer, you will develop and maintain our central data ecosystem, ensuring we extract maximum strategic value from our global data assets. You will be responsible for building, scaling, and governing high‑value data products that serve as the "single source of truth" for team.blue and its 60+ subbrands. This role is pivotal in transforming raw data into a sophisticated semantic layer that powers business intelligence, operational efficiency, and cutting‑edge AI initiatives.
Key Responsibilities
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- Data Platform Ownership: Own, manage, continuously optimize our Data Infrastructure including Databricks, Airflow, dbt, Gitlab & AWS. Manage and tune data platforms for peak performance and introduce new capabilities to keep the platform aligned with industry advancements
- Data Product Innovation: Design and deliver high-quality, scalable data products that provide a unified source of truth across a complex ecosystem of 60+ global sub-brands.
- Pipeline Engineering: Build and optimize robust ETL/ELT pipelines to ingest and transform massive volumes of structured and unstructured data, ensuring high availability and low latency for downstream consumers.
- Architectural Leadership: Lead the design of secure, cost-efficient data architectures. Establish and enforce best practices in data modeling, orchestration, and observability.
- Governance & Integrity: Implement rigorous data governance frameworks, including lineage tracking, metadata management, and quality controls to ensure a reliable semantic layer for AI and ML use cases.
- Cross-Functional Collaboration: Act as a strategic "sparring partner" for Analytics, ML, and AI teams, translating complex business requirements into high-performing technical solutions.
- Platform Optimization: Manage and tune data platforms for peak performance, focusing on indexing strategies, query optimization, and schema evolution.
- Mentorship: Elevate the collective expertise of the engineering team by mentoring junior members and fostering a culture of technical excellence.
Your Strengths
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- Strategic Thinker: You don't just execute; you lead projects from ideation to actionable solution, balancing technical debt with rapid delivery.
- Effective Communicator: You possess the ability to translate complex technical concepts into clear business value for non-technical stakeholders.
- Problem Solver: You thrive in fast‑paced environments, managing multiple high-priority workstreams with meticulous attention to detail.
Technical skills required
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- Core Ecosystem: Deep hands-on experience with Databricks (PySpark, Delta Lake, Unity Catalog).
- Advanced SQL & Modeling: Expert-level SQL skills (optimization, indexing) and a mastery of data modeling techniques (Dimensional, Star Schema, Snowflake).
- Cloud & Modern Data Stack: Proficiency in at least one major cloud provider (AWS, GCP, etc) and modern orchestration tools like Airflow or dbt.
- Database Diversity: Proven experience managing data across various RDBMS platforms (PostgreSQL, SQL Server, Oracle, etc).
- DevOps & Engineering: Strong command of Docker/Kubernetes and a commitment to CI/CD best practices.
- Governance: Experience with data versioning, schema evolution, and distributed metadata management.
Education and work experience
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- Experience: 7+ years of professional experience in data engineering or data management, preferably within a high-growth or multi-brand industry.
- Education: Advanced degree (Masters or PhD) in Computer Science, STEM, or a related quantitative field.
- Track Record: A demonstrable history of deploying stable, h