Engineering Manager - Data Platform (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?

As Engineering Manager, Data Platform at Statista, you’ll lead the engineers building a platform-as-a-product that helps teams manage and share trusted data. You’ll make it easier for data producers to get started while ensuring the platform is secure, reliable and cost-conscious as it grows.

You’ll work closely with the Senior Platform Product Manager: they lead product vision and priorities; you lead the people, technical delivery and day-to-day operation of the platform. The role spans Snowflake-based data infrastructure and event streaming via Kafka.

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• At least five years of experience leading engineering teams, ideally in data platforms, platform engineering or infrastructure. You know how to develop senior engineers and unite people with different technical backgrounds.

• A strong foundation in data or platform engineering, with the confidence to contribute to architecture discussions and design reviews. You're familiar with cloud data warehouses, ideally Snowflake, and tools or practices such as dbt and orchestration.

• Hands-on experience with infrastructure as code (IaC) using Terraform. You're comfortable with Kubernetes, CI/CD and AWS.

• Experience operating event-streaming systems such as Kafka, Amazon MSK, Confluent or Redpanda, including a schema registry.

• You understand data governance, including access models, data contracts, catalogues and lineage. You balance useful safeguards with a straightforward experience for the teams using the platform.

• You use AI tools and agentic workflows in your daily work and help your team do the same. You understand how AI agents consume data and know how to make the platform and its data assets work well for agents accessing them through MCP or A2A.