Cloud Data Engineer

📋 Description

• • Architect and own cloud-native data pipelines that ingest, cleanse, and transform millions of enrollment, claims, and wellness records every day. You will leverage Azure Data Factory, Databricks, and Python to ensure data is accurate, timely, and ready for downstream analytics that directly affect how quickly a member can select, change, or renew coverage.
• • Design and maintain an enterprise-grade data lakehouse on Azure that unifies structured and unstructured data from internal core systems, third-party administrators, and emerging IoT health devices. Your work will enable actuaries, data scientists, and product teams to run predictive models that anticipate member needs and reduce cost of care.
• • Automate critical enrollment and renewal workflows using Logic Apps, Azure Functions (.NET), and serverless event grids. By replacing manual spreadsheets and batch jobs with real-time, event-driven processes, you will cut onboarding time for new groups from days to minutes and eliminate costly coverage gaps.
• • Establish rigorous data quality and governance frameworks that satisfy HIPAA, HITRUST, and state regulatory requirements. You will implement data cataloging, lineage tracking, and automated anomaly detection so that every dataset consumed by member portals, call-center tools, and executive dashboards is trustworthy and compliant.
• • Partner with product owners, UX designers, and actuarial analysts to translate business requirements into technical user stories. You will lead sprint planning, provide accurate LOE estimates, and mentor junior engineers on best practices for scalable ETL/ELT patterns, CI/CD, and test-driven development.
• • Optimize compute and storage costs by tuning Databricks clusters, applying Delta Lake optimizations, and implementing lifecycle policies for Azure Blob and Data Lake storage. Your cost-aware mindset will help the organization reinvest savings into member-facing innovation.
• • Champion DevOps and MLOps culture by building reusable CI/CD pipelines in Azure DevOps or GitHub Actions that automate build, test, and deployment of data pipelines and machine-learning models. You will also create self-service templates so other teams can onboard new data sources in hours, not weeks.
• • Collaborate with cybersecurity and risk teams to embed encryption, tokenization, and role-based access controls into every layer of the data platform. Your proactive stance on security will protect sensitive PHI while enabling secure data sharing with providers and state agencies.
• • Experiment with Azure AI services—such as Azure OpenAI and Cognitive Services—to enrich member data with sentiment, risk scores, and next-best-action recommendations. Your prototypes will evolve into production features that personalize the member experience and improve health outcomes.
• • Continuously monitor pipeline health using Azure Monitor, Application Insights, and custom alerting dashboards. When issues arise, you will perform root-cause analysis, implement fixes, and conduct blameless post-mortems that strengthen platform reliability and team knowledge.