Platform Data Engineer

We are looking for a Senior (Lead) Platform Data Engineer to join our Helsinki team. Depending on experience, this role may be filled at a Senior or a Lead Platform Data Engineer level. At Sievo, data is everything — we have visibility into more than 2% of global GDP in spend, and turning data at that scale into dependable products takes a serious engineering foundation. As a Platform Data Engineer, you will build and operate the systems and data pipelines that foundation is made of: reliable pipelines that process and package large datasets, controlled and reproducible environments, and the automation and quality checks that keep everything trustworthy as the system scales. This role sits at the intersection of data engineering, software engineering, and platform ownership. You will design and build maintainable pipelines, own the environments and infrastructure they run on, and apply solid software engineering practices — version control, testing, automation, and reproducibility — so the products built on top are dependable. Think of it as a data engineer with a product-development mindset and the engineering rigor of a software engineer. You will work closely with data scientists, data engineers, product managers, and other engineering teams — gathering technical requirements, making sensible architectural trade-offs, and sharing ownership of the platform you help build. This is not a single-project role: in the longer term the work reaches across teams, giving you a hand in shaping Sievo's data architecture as a whole. Build and maintain data pipelines that process and move large datasets between systems, with a strong emphasis on maintainability and reliability Set up and own controlled, reproducible environments and cloud resources — permissions, deployment patterns, infrastructure-as-code, security, scalability, and cost efficiencyDesign data architecture and processing flows, building pipelines in code rather than relying solely on cloud drag-and-drop toolingOperate and monitor what you build — instrument systems with the metrics and checks that show whether they are healthy, and act on what they tell youAutomate data processes and recurring runs that are manual today, and set up new parts of the data environment as Sievo's data work growsApply software engineering practices throughout: Git-based version control, CI/CD-style workflows, automated testing, and reproducible, production-quality codeDefine and implement data quality and validation checks so downstream outputs are trustworthy and understandableGather technical requirements from internal stakeholders, communicate trade-offs clearly, and work with the team to take deliverables through to productionTroubleshoot data quality and integration issues, fixing root causes rather than symptoms.