Senior Data Platform Engineer (m/f/d)

At Statista, we’re all about facts and data, for we are the world's leading business data platform. By providing reliable and easy-to-use data as well as various data analytics products and services, we empower people worldwide to make fact-based decisions.

Founded in Hamburg in 2007, we have quickly grown into a global company with offices in major cities such as London, New York, Berlin and Tokyo. And we still have a lot of plans. Our constant growth does not only prove our success, but also keeps creating new development and career opportunities for our employees.

We value and celebrate our diverse culture. You are welcome here for who you are, no matter where you come from, what you look like, or whether you prefer bar graphs to pie charts. Your story matters – keep writing it as part of our team.

Are you ready to join us?

• Own the platform architecture. Shape and evolve the target architecture across infrastructure utilities, data product experience and federated governance, together with Data Architecture and Product.

• Build the Platform-as-a-Product. A standard ingestion framework, roles and permissions, cost monitoring per product and team, and agent/AI readiness.

• Make governance computational. Data contracts, quality gates, lineage and policies as code, enforced in the pipeline instead of by review. Monitoring, alerting, SLAs and incident handling.

• Deliver data assets to consumers. Standard output ports (API, MCP server, A2A, Kafka events) and a OpenMetaData catalogue for humans and agents.

• Work with domain teams and stakeholders. Reference products, onboarding playbook and hands-on support for data domain teams. Bring the technical view into stakeholder discussions.

• 5+ years in data or platform engineering

• Hands-on depth in Snowflake and AWS

• Built Infrastructure as code and CI/CD for data workloads with Terraform

• Built self-serve capabilities other teams use without hand-holding: templates, config-driven onboarding, clear docs, policies as code and cost governance

• Data governance in practice: data contracts, quality checks, lineage, access management, data catalogues

• Strong communication with non-engineers; comfortable defending architecture decisions in cross-team steering