Data Lead

Posted about 1 month agoViewed

💎 Seniority level: Lead, 5+ years

📍 Location: UK, Germany

🔍 Industry: Software Development

🏢 Company: Gitpod

🗣️ Languages: English

⏳ Experience: 5+ years

🪄 Skills: SQLData AnalysisETLData engineeringData visualizationData management

Experience being the primary owner of a data function, ideally in a fast-growing startup or scale-up environment
Built or significantly evolved data platforms that directly supported business growth
Track record of successfully wearing multiple hats across the data stack - from data engineering to analytics. Examples of projects spanning different areas of the data lifecycle
Experience with modern data stack tools like dbt, Prefect, Census or other orchestration and reverse ETL tools, modern warehouses (ideally BigQuery)
Background working with product analytics and data from multiple other first/third party data sources, experience building up a customer data platform
Evidence of stakeholder collaboration and business impact
Basic JavaScript experience

Own and scale Gitpod's data platform end-to-end, from data ingestion, warehousing, and transformation to analytics and visualization. Make architectural decisions that balance immediate needs with future scalability while maintaining high reliability and cost efficiency.
Drive data-informed decision making across the company by building and maintaining core business metrics, partnering with teams to define KPIs, and creating self-service analytics capabilities.
Establish and maintain data engineering best practices including documentation standards, testing procedures, monitoring systems, and disaster recovery processes. Build reusable components and frameworks that enable sustainable scaling of our data function.
Enable our enterprise sales motion by developing systems to track customer behavior, evolving our lead enrichment pipelines and ensuring marketing, sales and customer experience have the insights and data they need in the right places.
Partner cross-functionally to understand data needs, implement solutions, and communicate insights effectively. Train and enable internal teams on self-service analytics tools while maintaining high standards for data quality and accessibility.