Position Summary

The Senior Data Engineer is responsible for building and operating the data infrastructure that powers RxSense’s analytics, AI systems, and business intelligence capabilities. You will work across Snowflake and SQL Server to design pipelines that move claims data, pricing data, and clinical information from operational systems into governed, accessible, and AI-ready formats. This role is critical to our AI-native transformation—every agentic system, pricing model, and clinical intelligence capability depends on reliable, well-governed data. You will also play a key role in solving longstanding data access and governance challenges, building the foundations that enable both humans and AI agents to work with data safely and efficiently.

Key Responsibilities

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.

Requirements

• 5+ years of professional data engineering experience with strong proficiency in SQ