Data Architect

Role & responsibilities

• Architectural Strategy: Define and lead the end-to-end data architecture strategy, ensuring alignment with long-term business goals and advanced analytics requirements.
• Strategic Data Design: Design and implement complex conceptual, logical, and physical data models optimized for high-performance analytics and reporting.
• Medallion Architecture: Architect scalable Lakehouse environments using Bronze, Silver, and Gold layer patterns to ensure data quality and lineage.
• Governance & Standards: Establish enterprise-wide data standards, including metadata management, data security protocols, and master data management (MDM).
• AI Readiness: Partner with Data Science teams to ensure data architectures are optimized for LLM integration, Vector databases, and RAG pipelines.
• Cross-Functional Leadership: Orchestrate collaboration between data engineers, business consultants, and stakeholders to translate business logic into technical blueprints.

Preferred candidate profile

• Professional Experience: Minimum 7+ years in Data Architecture or Senior Data Engineering within an Analytics-heavy environment.
• Data Modeling Mastery: Extensive experience in Logical and Physical data modeling techniques, including 3NF, Dimensional Modeling (Star/Snowflake), and Data Vault 2.0.
• Reverse Engineering: Proven expertise in Reverse Engineering techniques to document and modernize legacy database schemas into optimized target architectures.
• Modeling Stacks: Expert proficiency in industry-standard tools such as ER/Studio, Erwin Data Modeler, or Lucidchart for designing relational and non-relational structures.
• Cloud & Big Data: Hands-on experience architecting solutions on Azure (Fabric/Synapse), AWS

(Redshift/Glue), or Snowflake, utilizing Delta Lake and Spark-based processing.

• Advanced Database Systems: Deep knowledge of SQL (PostgreSQL, SQL Server) and NoSQL systems,

along with Vector Databases (Pinecone, Milvus) for AI-driven applications.