Data Architect
Skills & requirements
PythonSQLAbout the role
About the Role
We are seeking a Data Architect to design, develop, and govern data architecture across the organization. You will work with business and technical leaders to translate business requirements into scalable, well-structured data solutions that support enterprise-level decision-making and data-driven initiatives.
Key Responsibilities
Develop conceptual, logical, and physical data models with focus on structure, semantics, constraints, and alignment to business objectives
Define and maintain reference and target data architectures across operational systems, event/streaming platforms, and analytical environments (data warehouse/lakehouse)
Drive architectural decision-making on consistency, performance, scalability, cost, operability, and security, with comprehensive documentation
Support implementation of organizational data strategy, including database design standards and analytics platform integration
Partner with project teams as an embedded contributor to guide schema design, physical design choices, data access patterns, and query optimization
Advise on end-to-end data product lifecycle from platform selection through testing and implementation
Define data flows and integration patterns across applications, services, operational systems, and analytical platforms
Govern changes to architectural artifacts and ensure compliance with data architecture guidelines
Requirements
BSc/MSc in Computer Science, IT, or related field
Minimum 3 years in data modelling, data engineering, database design, or data architecture
Strong expertise in conceptual, logical, and physical data modelling with solid SQL skills
Proficiency in Python for data automation, prototyping, validation, and data quality checks
Experience with cloud-based data platforms and modern data warehouse/lakehouse architectures
Understanding of software development lifecycles, requirements analysis, and technical design best practices
Collaborative approach with ability to build trusted relationships across teams
Exposure to data governance, metadata management, and data quality frameworks
Confidence in ETL/ELT patterns, data integration, and data orchestration technologies
