Senior Data Architect

SwingDev—a hippo company is the Polish branch of a fast-growing InsurTech product company – Hippo Insurance. The company focuses on making home insurance smarter and more proactive using technology. The Senior Data Architect will be part of the central data function, responsible for establishing the company's data architecture and modeling foundations from the ground up, working on greenfield projects with full ownership.

• 8+ years of experience in data architecture, data modeling, or analytics engineering.
• Strong hands-on experience with modern data platforms and modeling techniques.
• Experience with cloud platforms such as GCP, AWS, or Azure and tools like Databricks, BigQuery, Redshift, or Snowflake.
• Knowledge of building data warehouses, lakehouses, pipelines, and large-scale data ecosystems.
• Understanding of AI-ready data foundations supporting analytics and ML.
• Familiarity with medallion architecture, data governance, observability, and data catalogs.
• Ability to act as a decisive technical expert guiding senior engineers and influencing architectural direction.
• Excellent communication skills, using storytelling to simplify complex topics.
• Comfortable shaping standards and best practices from scratch.
• Curiosity about RAG, enterprise AI agents, and modern data architecture patterns.
• Commitment to security, responsible data practices, and maintaining high quality.
• Self-starter with ownership mindset, able to establish architecture and modeling practices with minimal oversight.
• Proficient in English and Polish (written and spoken).
• Flexible availability for occasional evening meetings to coordinate across PL-US teams.

Nice to have:

• Experience from large organizations with mature architecture teams or from the insurance industry.

• Define and own enterprise-wide data architecture and modeling standards.
• Design conceptual, logical, and physical data models across core business domains.
• Build modern data platform components including pipelines, lakehouse/warehouse layers, and medallion-architected datasets.
• Create scalable, AI-ready data environments supporting analytics, data products, and future AI/ML initiatives.
• Partner with engineers, analytics teams, and business stakeholders to ensure data reliability, reusability, and security.
• Implement strong data governance, observability, and cataloging to enable self-service and support data mesh principles.
• Drive strategic technical decisions influencing platform and tooling choices with a modern, cloud-native mindset.
• Translate complex data and AI concepts into simple narratives for non-technical audiences.
• Advise on emerging AI architectures, including RAG patterns and enterprise AI agents.
• Shape a healthy, modern, future-proof data environment from the ground up without technical debt.