Azure Data Architect

Requirements

• Work closely with business stakeholders, solution architects, data engineering teams, and platform owners.
• Translate complex business requirements into technical components, evaluates and recommends architectural patterns; and guides the adoption of modern data technologies and standards.
• Responsible for researching and experimenting with emerging data platforms, defining data integration and governance frameworks, ensuring data quality and consistency, and supporting both legacy systems and modern cloud ecosystems.
• Accountable for data architecture delivery at the project and domain level; consults with and collaborates with business stakeholders, solution architects, and engineering teams.
• Gather, analyze, and validate business and technical data requirements; translate requirements into conceptual, logical, and physical data models with ownership and authority.
• Design and maintain enterprise data models, database schemas, and message models supporting transactional systems, ODS, data warehouses, and analytics platforms.
• Ensure data architecture and designs conform to enterprise reference architecture, data standards, naming conventions, and compliance rules.
• Define and maintain data integration architectures, patterns, and roadmaps aligned to enterprise data and integration strategy.
• Provide technical leadership for ETL and data integration design and development across Azure Data Factory, Synapse, and Informatica.
• Author and maintain source-to-target mappings, transformation logic, business rules, and data integration documentation.
• Partner with business and product owners to define KPIs, metric logic, and shared metric catalogs; ensure consistent interpretation and use across analytics and reporting.
• Create and maintain curated analytics datasets and semantic models (SQL views, tables, BI models) to support dashboards and self-service reporting.
• Perform data profiling, reconciliation, and validation using SQL and Python to ensure data accuracy, completeness, and consistency across systems.
• Define data validation rules and test scenarios; support QA, UAT, and production readiness activities.
• Participate in data governance processes, including metadata management, lineage documentation, certified datasets, and reduction of redundant data assets.
• Develop and maintain processes for capturing and managing metadata across operational, integration, and analytical platforms.
• Collaborate with application, infrastructure, and integration teams to ensure end-to-end information flow across systems and platforms.
• Lead or participate in architecture design sessions and architecture reviews; identify risks and recommend mitigation strategies.
• Provide technical coaching and mentoring to data engineers, data analysts, and junior architects; promote modeling and integration best practices.
• Support project planning, estimation, and delivery activities; contribute to technical designs and implementation approaches.
• Proactively research and apply modern data architecture patterns, cloud best practices, and emerging technologies to improve scalability and performance.

Benefits

• Competitive salary
• Opportunities for professional growth and development
• Collaborative and dynamic work environment