Senior Data Architect

O projekcie:
• 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

Wymagania:
- 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.

Codzienne zadania:
- 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.