Senior Data Architect

Prestigious position at one of the world’s largest banks. Stable, long-term projects. Hybrid work (6 days per month from the office in Cracow) and flexible working hours.

• Strong (10+ years) data architecture and data modelling experience (conceptual/logical/physical), including documenting complex data structures and relationships.
• Proficiency in SQL and Python, ideally in Google Cloud Platform or other cloud platforms, with the ability to validate data designs through analysis.
• Solid knowledge of data management best practices: metadata, lineage, data quality, security/privacy, retention, and governance.
• Experience defining architecture patterns and standards for data pipelines, integration, and publishing/consumption layers.
• Strong English language skills for clear written and verbal communication, including producing architecture documentation and facilitating design discussions.

Nice to have:

• Understanding of IT infrastructure or business architecture and how data architecture aligns to broader enterprise architecture.
• Experience in large or matrix organisations with multiple stakeholders and delivery teams.
• Familiarity with data governance and metadata tools (e.g., Collibra, Erwin, Dataplex) and cloud environments (Azure, AWS, Google Cloud Platform).
• Strong technical writing and presentation skills, including the ability to communicate architecture decisions and trade-offs to varied audiences.

• Define and maintain enterprise and domain data architecture standards, including canonical models, naming conventions, metadata, and reference data patterns.
• Own the target-state data architecture for Data Refinery, including conceptual, logical, and physical data models and end-to-end data flows.
• Design scalable data integration patterns (batch/streaming), ensuring interoperability across multiple source systems and platforms.
• Establish and govern data quality, lineage, retention, and protection requirements for published datasets, partnering with engineering and governance teams to implement controls.
• Translate business and functional requirements into architecture artefacts (e.g., data models, interface contracts, data flow diagrams, and non-functional requirements).
• Act as the data architecture SME for engineers, testers, product owners, and business users, guiding design decisions and resolving data design issues.