Senior Data Platform Engineer
我们的商业团队。蓝色是一个成功品牌生态系统，在各个地区合作为客户提供他们在线取得成功所需的一切。60 多家成功品牌组成该集团；拥有3000多名专家组成的团队服务于欧洲及其他地区的350万客户。team.blue 的品牌是传统托管企业的混合物，提供从域名，电子邮件，共享托管，电商和服务器托管解决方案等服务，以及专门的 SaaS 供应商提供的相邻产品如合规性，营销工具和团队协作产品。这一广泛的产品产品提供使其成为欧洲各地的网络企业和创业者的一站式合作伙伴。作为高级数据平台工程师，你将开发和维护我们的中心数据生态系统，确保我们从全球数据资产中提取最大的战略价值。你将负责构建，扩展和管理高价值数据产品，这些产品是 team.blue 及其60多个子品牌的"唯一来源".这一角色在转化原始数据为一个复杂的语义层中发挥着关键作用，它提供了商业智能，运营效率和尖端 AI 倡议。主要责任：· 数据平台所有权：拥有，管理，持续优化包括 Databricks, Airflow, dbt, Gitlab & AWS 在内的数据基础设施。管理和调整数据平台以实现最高性能，并引入新的功能来保持该平台与行业进步一致 • 数据产品创新：设计和提供高质量的可扩展的数据产品，在60多个全球子品牌复杂生态系统中提供统一的真相来源。•管道工程：构建和优化强大的 ETL/ELT 管道，以吸收和转换大量结构性和非结构性的数据，确保下游消费者获得高可用性和低延迟时间。•建筑领导力：领导安全，成本效率高的数据架构的设计。建立和执行数据建模，编排和可观察性方面的最佳实践。• 治理与完整性：实施严格的数据治理框架，包括谱系跟踪，元数据管理和质量控制以确保 AI 和 ML 使用情况的可靠语义层。• 多功能合作：作为分析，ML 和 AI 团队的战略"保护伙伴",将复杂业务要求转化为高性能技术解决方案。• 平台优化。
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Our Business
team.blueis 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:
· 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.
YourStrengths:
• 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.
Technicalskills required:
• 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.
Educationand work experience:
• 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, high-performance data solutions that drive measurable business value.
Right to Work
At any stage, please be prepared to provide proof of eligibility to work in the
country you’re applying for. Unfortunately, we are unable to support relocation
packages or sponsorship visas
"Come as you are"
Everyone is welcome here.Diversity & Inclusion are at our core. Far above any technical competence, we value respect, openness, and trusted collaboration. We do not tolerate intolerance.
ESG
"Atteam.blue, our commitment to caring for the environment and each other is at the heart of everything we do. Our latest impact report showcases our ongoing ESG efforts and ambitious sustainability goals. Interested in learning more about our dedication to making a positive impact? Check it outhere.”
Originally posted on Himalayas