Data Architect

• 15+ years of experience in enterprise architecture / solution architecture, with significant experience in Pharma Commercial (Sales, Marketing, Market Access, or Commercial Analytics).
• Proven experience evaluating technology products and defining selection criteria (security, scalability, integration feasibility, TCO, vendor fit).
• Strong hands-on experience designing and governing integrations (APIs, event streaming, batch/file integrations, middleware/iPaaS), including non-functional requirements (performance, resiliency, observability).
• Strong understanding of sales planning, forecasting, bid management, and tender management concepts (e.g., targets, territories, quotas, demand/forecast inputs, actuals vs plan, scenario planning, tender lifecycles, pricing submissions, and contract award workflows).
• Strong data modelling skills (dimensional modelling, star schema, semantic modelling) and ability to translate business KPIs into governed metrics.
• Experience building BI/analytics applications and semantic layers (e.g., Power BI, Tableau, Qlik or similar) and enabling self-service with governance.
• Experience reviewing technical artifacts (HLD/LLD, interface specs, data models, architecture diagrams) and providing actionable review feedback.
• Excellent communication skills with ability to influence engineering teams and business stakeholders.
• Bachelor's degree in Engineering, Computer Science, Information Systems, or related field (or equivalent experience)., * Experience with commercial ecosystems and platforms such as Salesforce (Sales/Revenue Cloud), Veeva, tender and bid management solutions, marketing analytics platforms, and integration with enterprise ERP and pricing/GTN systems.
• Experience on cloud data platforms (Azure/AWS/GCP) and data engineering patterns (lakehouse, ELT/ETL orchestration, metadata management).
• Exposure to syndicated pharma datasets and commercial data sources (e.g., IQVIA, Symphony) and common commercial KPIs.
• Knowledge of MDM concepts (customer/product hierarchies), data quality frameworks, and governance operating models.
• Experience with architecture review boards, enterprise standards, and regulated environments (auditability, segregation of duties, secure data handling).

Key Skills & Competencies

• Strong architecture thinking with pragmatic delivery focus (ability to translate target state into incremental releases).
• Analytical mindset with attention to detail for data models, metric definitions, and technical specifications.
• Ability to review work products objectively and raise risks early with mitigation options.
• Collaboration and stakeholder management across business, data, and engineering teams.
• Strong documentation discipline and ability to drive consistency in artifacts and standards.