Senior Data Architect

Verita HR KRAKÓW 2026-08-19

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 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.

• Prestigious position at one of the world’s largest banks

• Stable, long-term projects

• Competitive salary with a B2B contract

• Hybrid work (6 days per month from the office in Cracow) and flexible working hours

• Private healthcare and multisport card

• Personal growth and development opportunities with the possibility to rotate between projects

• Referral program and company events

• Convenient parking ,[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.] Requirements: SQL, Python, GCP Additionally: Sport subscription, Private healthcare, Free coffee, Gym, Bike parking, Playroom, Shower, Free beverages, Mobile phone, Modern office, No dress code.