Data Architect

Job description

**Role & responsibilities** We are seeking a highly experienced **Data Architect** working on design, governance, and optimization of healthcare and health insurance data models supporting configurable product platforms and enterprise data ecosystems. The ideal candidate will possess deep expertise in Data Modelling, Python, SQL, and R along with knowledge in healthcare payer and provider data, product modelling, healthcare interoperability standards, and enterprise data architecture. This individual will work closely with Product Management, Solution Architects, Business Analysts, Engineering, and Client Delivery teams to design scalable canonical data models that enable rapid product configuration, API-driven integrations, analytics, and regulatory compliance. This role requires strong experience working within highly regulated industries handling sensitive healthcare and insurance data, ensuring data quality, governance, traceability, security, and compliance throughout the product lifecycle. **Key Responsibilities** **Technology** Python, R, Data Modelling Tools, PostgreSQL and AWS **Product & Enterprise Data Modelling** - Design conceptual, logical, and physical enterprise data models supporting configurable healthcare and insurance products. - Model healthcare product lifecycle data, benefit plans, provider networks, member enrollment, claims, eligibility, pricing, underwriting, and policy administration. - Define reusable canonical data models enabling enterprise-wide consistency across products and client implementations. - Collaborate with Product, Engineering, and Client Services teams to rapidly understand product configuration requirements and translate them into scalable data models. - Maintain metadata repositories, business glossaries, data dictionaries, and ERDs. **Data Analysis & Engineering** - Perform data profiling, impact analysis, and exploratory analysis across complex healthcare datasets. - Develop SQL and Python-based solutions for data validation, transformation, reconciliation, and automation. - Collaborate with Data Engineering teams supporting ETL/ELT pipelines, cloud data platforms, and analytical environments. **Stakeholder Collaboration** - Partner with Product Owners, Architects, Business Analysts, Client Delivery, and Engineering teams. - Lead design reviews, modelling workshops, and architecture discussions. - Mentor junior data modelers and establish enterprise modelling standards.