Senior Data Architect

We are a team that develops and maintains analytical solutions for various verticals (finance, telco, media, retail, manufacturing & others). We are building a modern data ecosystem based on a broad range of technologies and the cloud services. 

• Define the enterprise data architecture vision aligned with business strategy and digital transformation goals.
• Design conceptual, logical, and physical data models for domain-driven systems.
• Establish target-state architectures for data platforms (cloud, hybrid, on-prem).
• Evaluate and select core technologies, taking into consideration specific customer needs, priorities and current architecture
• Architect scalable data pipelines (ETL/ELT).
• Define integration standards (APIs, CDC, event-driven architectures).
• Optimize data lake, lakehouse, and warehouse architectures.
• Design high availability, disaster recovery, and backup strategies.
• Enforce security controls: encryption, IAM, row-level security, masking.
• Evaluate emerging paradigms (data mesh, lakehouse, streaming-first architectures).
• Assess tools for orchestration and transformation (e.g., Apache Airflow, dbt).
• Prototype proof-of-concept architectures.
• Ensure architectural flexibility for AI, advanced analytics, and real-time insights.

• Experience in data modeling (conceptual, logical, physical), preferably in a large organization or within complex data ecosystems.
• Practical knowledge of tools and methodologies such as ER, UML, IDEF1X, Data Vault, 3NF, Kimball/Inmon.
• Ability to work with data catalogs and metadata management tools (e.g. DataHub, Collibra, Alation, Atlan).
• Knowledge of SQL and relational data models.
• Experience working with database systems and data architecture in a cloud environment
• Ability to work with engineering and analytics teams to translate business needs into data models.
• Understanding of data security, availability, and control principles.
• Good command of English (reading documentation, community discussions).

• Experience in Data Governance, Data Quality, Master Data, and Metadata Management projects. ​
• Knowledge of concepts and technologies related to the semantic data layer: ontologies, RDF, OWL, GraphQL, dbt Semantic Layer, Semantic Kernel. ​
• Knowledge of Python for automation, model validation, and API integrations. ​
• Knowledge of event-driven data architecture. ​
• Experience in creating and maintaining architectural documentation.