Posted:2 days ago|
Platform:
On-site
Full Time
Work with us to build modern Insurtech AI underpinned solutions, we are a growing team of hands on architects striving to build high quality solutions for our internal and external customers. The Data Architect is responsible for designing enterprise data solutions including data warehouses, data lakes, and analytics platforms. This role defines data strategy, establishes data governance, and architects scalable data infrastructure.
Data Modeling & Design - Designs sophisticated data models using dimensional modeling (star/snowflake schemas) for analytics, data vault methodology for enterprise warehousing with audit trails critical for insurance, and normalized models for operational systems. Creates master data management solutions ensuring single sources of truth for policies, claims, customers, agents, and products. Establishes naming conventions, data standards, and metadata practices enabling data discoverability and self-service analytics.
Analytics & AI/ML Enablement - Designs platforms optimized for analytics and AI/ML workloads, creating feature stores for reusable ML features, data science sandboxes with governance, and training pipelines supporting insurance-specific models for fraud detection, claims prediction, and underwriting automation. Ensures architectures support traditional BI and advanced analytics including NLP on policy documents and computer vision for claims assessment.
Seven or more years in data engineering, data architecture, or database administration roles with demonstrable emphasis on cloud data platforms, including at least three years designing and implementing enterprise-scale data solutions. Proven experience architecting solutions on Microsoft Azure data services with preference for candidates having led migrations from on-premises to cloud or multi-cloud data platform implementations. Prior experience in designing lakehouse style architectures.
Track record of designing data warehouses and data lakes for complex organizations, preferably in insurance or financial services environments with strict regulatory requirements. Hands-on experience implementing data governance frameworks, establishing data quality standards, and building data catalogs enabling self-service analytics. Demonstrated success architecting data platforms supporting AI/ML workloads including feature engineering pipelines and model training datasets.
Experience leading cross-functional data initiatives, collaborating with business stakeholders to translate requirements into technical solutions, mentoring data engineering teams, and establishing architectural standards adopted across organizations. Evidence of optimizing data platform costs while maintaining performance SLAs and implementing DataOps practices including automated testing, CI/CD for data pipelines, and monitoring/alerting frameworks.
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