RxSense - Senior Data Engineer

• Data Pipeline Design and Operations • Design, build, and maintain production-grade ETL/ELT pipelines that move data between SQL Server (operational), Snowflake (analytical), and downstream consumers including AI systems, reporting tools, and business intelligence platforms. • Optimize data ingestion and transformation patterns for healthcare-scale volumes—millions of claims, pricing transactions, and member records processed daily. • Implement data quality checks, validation rules, and monitoring that catch issues before they propagate to analytics, AI models, or regulatory reports. • Build and maintain data models in Snowflake that support self-service analytics, enabling product, clinical, actuarial, and operations teams to answer their own questions. • Manage pipeline scheduling, orchestration, and SLA monitoring to ensure data freshness targets are met across all business-critical data products. • Data Governance and Access • Implement role-based access controls (RBAC) and data governance frameworks that enable squad-level and group-level data access rather than ad hoc individual permissions. • Build and maintain data catalogs and lineage documentation that make it clear what data exists, where it comes from, what transformations have been applied, and who has access. • Design data access patterns specifically for AI agents, ensuring agents can retrieve the data they need with appropriate authorization, audit trails, and containment boundaries. • Ensure all data infrastructure complies with HIPAA requirements, including data de-identification for non-production environments, PHI access logging, and encryption at rest and in transit. • Collaborate with security and IT teams to implement secrets management best practices for database credentials, API keys, and service accounts used in data pipelines. • Platform and Infrastructure • Architect Snowflake environments for cost-effective performance, including warehouse sizing, clustering, materialized views, and query optimization strategies. • Support the lower-environment data strategy by implementing alternatives to full production data replication, including data subsetting, synthetic data generation, and lookback-window-based approaches. • Collaborate with DevOps and infrastructure teams on AWS-based data infrastructure, including S3 storage optimization, IAM policies for data access, and cost management across data storage tiers. • Evaluate and implement data integration tools and frameworks that reduce pipeline development time while maintaining reliability and observability. • AI and Analytics Enablement • Partner with the AI team to build data foundations for AI workloads, including feature stores, training data pipelines, and governed access to claims and pricing data for model development. • Build data pipelines that support real-time and near-real-time use cases for AI-driven pricing, claims analysis, and clinical intelligence. • Develop data products that leverage RxSense’s longitudinal claims data as a compounding competitive advantage—enabling trend analysis, formulary optimization, and cost management insights. • Support the development of financial visibility tools that enable reporting on per-customer cost and spend, closing a critical gap in current business intelligence capabilities.