Data Engineer, TBAuctions

• Strong Python skills, with the habits that make code maintainable: tests, structure, review
• Working experience with Airflow or a comparable orchestrator
• Experience on Azure, and with Azure DevOps for CI/CD (Pipelines, and Artifacts for shared packages)
• Databricks (incl. Asset Bundle deployment) or similar data platform tool experience
• Terraform, or experience with an alternate infrastructure as code tool and a willingness to learn
• Docker, and comfort with how containers behave in production vs development
• Familiarity with DBT best practices and implementation
• An understanding of how to tackle different extraction sources, such as databases, service, API endpoints, etc
• API design and integration (e.g. FastAPI)
• Real comfort in the terminal: git, shell, debugging a process on a box/container you've SSH'd into, without reaching for a GUI
• Enough networking to be useful. DNS, TLS, firewall rules, private endpoints, VNets. You don't need to be a network engineer, but "it's a networking problem" shouldn't be where you stop
• You can design a system and explain it: a clear architecture diagram and design document that a mixed audience of engineers and non-engineers can both follow
• You design in the open. You write proposals, you invite review early, and you change your mind when someone makes a better argument
• You can sit with a stakeholder who doesn't know what they want yet and leave the room with a specification. Running a workshop shouldn't scare you
• You communicate comfortably in English, in writing, with colleagues across several countries

Desirable

• You'll join an established Data Team as a Data Engineer, working alongside fellow Analytical Engineers and Data Analysts, building and running the pipelines and infrastructure that move data from a variety of different systems (e.g. Services such as Bloomreach, API sources and a variety of databases) into Databricks and out to the people and products that use it
• This is a hands-on engineering role with real ownership. You'll pick up components end to end, from the conversation with the stakeholder about what they actually need, through the design, to the provisioning of the infrastructure, ending with the orchestration of the different processes in a reliable, visible and cost-effective way
• This is also a role where new systems can and will come into play. Being able to integrate said systems into the Data Platform in a modular manner that upholds the strengths and principals of the platform while delivering the intended value is a key challenge
• Build and maintain ETL data pipelines in Python, orchestrated with Airflow, processing data in Databricks on Azure
• Define infrastructure as code with Terraform, and ship it through Azure DevOps pipelines with shared libraries published as Artifacts
• Build and integrate APIs (FastAPI or similar) to make data available to other teams and services
• Containerize workloads with Docker and keep them running reliably in production
• Write the design down before you build it: architecture diagrams and documentation that someone else can follow six months from now
• Take part in our RFC process. Propose designs, review your colleagues', and disagree constructively
• Work directly with fellow Analytical Engineers and Data Anlaysts within the Data Team, external stakeholders across the brands and central functions to gather requirements, run through options and translate what they ask for into what they need
• Own the operational side of what you build: monitoring, cost, data quality, incident response