Senior Healthcare Data Engineer

Epic Certified Analytics / Data Engineer (Clarity & Caboodle) Location: Boston, MA Employment Type: Contract Role Overview The Epic Certified Analytics/Data Engineer will serve as the organization’s technical authority responsible for extracting and migrating data from cloud-hosted Epic Clarity and Caboodle environments into the enterprise Bronze data layer. This individual contributor role requires Epic certification to legally and technically query the hosted Epic environment and securely land source-system data into structured storage environments. The position is foundational to the organization’s modern data architecture and analytics strategy, enabling downstream Silver/Gold transformations and enterprise reporting initiatives. This role will partner with IT, analytics, security, and platform engineering teams to ensure reliable, governed, and scalable data ingestion pipelines. Success in this role establishes the backbone for enterprise-wide data visibility and advanced analytics capabilities. Employee Value Proposition Purpose This role sits at the architectural foundation of the company’s data modernization strategy. Rather than simply running reports, this individual will unlock access to one of the most critical healthcare data assets in the organization and enable the entire analytics ecosystem. Growth As enterprise data architecture matures, this role offers exposure to cloud data platforms, data governance strategy, and advanced AI-driven analytics enablement. The individual will have the opportunity to design scalable ingestion patterns that support machine learning, predictive modeling, and executive decision intelligence. Motivators This position is ideal for someone who enjoys solving complex data access challenges, building structured ingestion frameworks, and creating technical systems that power enterprise-wide impact. The satisfaction comes from engineering reliable pipelines that become the trusted source of truth across the organization. Major Performance Objectives 1. Establish Secure Epic Data Extraction and Bronze Layer Ingestion Framework (First 6 Months) Within the first six months, implement secure, compliant extraction processes to migrate designated Clarity and Caboodle datasets from the cloud-hosted environment into the enterprise Bronze data layer. Design and deploy repeatable ingestion pipelines using approved data engineering standards, ensuring data completeness, lineage tracking, and governance compliance. Deliver at least five high-priority domain datasets successfully landed in Bronze with documented validation controls and less than 1% reconciliation variance. This objective may be enhanced using AI-assisted schema discovery and automated metadata extraction tools to accelerate understanding of the Epic data model. 1. Design Scalable Data Engineering Standards for Epic-Sourced Data (6–9 Months) By month nine, architect standardized data ingestion templates, transformation frameworks, and orchestration workflows that allow additional Epic domains to be migrated efficiently and securely. Establish documentation, version control, and monitoring systems that reduce manual intervention and enable reliable scheduled loads. Success will be measured by reducing ingestion cycle time by at least 30% and achieving 99% pipeline reliability. AI-enabled pipeline monitoring and anomaly detection tools may be leveraged to proactively identify ingestion failures or schema changes. 1. Enable Downstream Analytics and Enterprise Reporting Through Structured Data Delivery (9–12 Months) Within the first year, collaborate with analytics and BI teams to ensure Bronze-layer data supports downstream Silver/Gold transformations, reporting, and advanced analytics initiatives. Deliver validated datasets with clearly defined business logic, lineage documentation, and reconciliation protocols. Measure success by improved downstream reporting accuracy, reduced data access delays, and stakeholder satisfaction scores indicating increased confidence in enterprise data availability. AI-based data quality scoring and automated reconciliation processes may enhance this objective. Critical Subtasks 1. Conduct Epic Environment and Security Assessment (First 30 Days) Within the first 30 days, evaluate the cloud-hosted Clarity and Caboodle architecture, security requirements, query limitations, and compliance constraints related to certified access. Document extraction boundaries, performance considerations, and governance requirements. Deliver a technical access and ingestion strategy approved by IT security and platform leadership. AI-enabled architecture documentation tools may assist in mapping dependencies and data lineage. 1. Develop Initial Extraction Scripts and Data Movement Processes (First 60 Days) Design and validate extraction scripts or ETL processes to securely pull source-system data into staging environments prior to Bronze ingestion. Implement structured logging, auditing, and validation checkpoints to ensure data completeness and compliance. Achieve successful migration of pilot datasets meeting accuracy and timeliness standards. AI-powered SQL optimization tools may enhance query performance and reduce development time. 1. Implement Bronze Layer Data Modeling and Storage Standards (First 3 Months) Define schema conventions, naming standards, partition strategies, and metadata tagging protocols for Epic-derived datasets entering the Bronze layer. Ensure alignment with enterprise data architecture guidelines and governance policies. Deliver formal documentation and obtain architecture committee approval. AI-assisted schema comparison tools may support consistency and change tracking. 1. Establish Monitoring, Reconciliation, and Data Quality Controls (Ongoing Through Year One) Develop automated reconciliation checks between Epic source systems and Bronze-layer datasets to maintain data integrity and detect anomalies. Implement monitoring dashboards tracking ingestion performance, latency, and failure rates. Achieve defined SLAs for pipeline uptime and data accuracy. AI-based anomaly detection and predictive monitoring tools may support proactive issue resolution. 1. Partner with Analytics and Platform Teams to Prioritize Domain Migration (Ongoing) Collaborate with analytics stakeholders to prioritize high-impact data domains and sequence ingestion work accordingly. Provide technical consultation regarding data availability, schema complexity, and transformation readiness. Success will be measured by stakeholder adoption rates and reduction in ad-hoc extraction requests. 1. Document Governance, Lineage, and Certification Compliance Standards (Ongoing) Maintain detailed documentation of certification requirements, access controls, lineage mapping, and audit logs to ensure ongoing compliance with Epic and enterprise policies. Conduct periodic reviews with security and compliance teams to ensure adherence to standards. AI-based documentation management tools may enhance audit readiness and traceability. 1. Continuously Evaluate and Integrate AI to Improve Performance (Within 90–180 Days and Ongoing) Within the first 90–180 days, proactively evaluate how AI and automation tools can enhance data ingestion, schema discovery, query optimization, reconciliation, and monitoring processes. Pilot AI-assisted development tools and implement scalable automation where measurable efficiency gains are identified. Embed continuous AI adoption into daily engineering workflows to improve speed, accuracy, and architectural scalability. Success will be measured by reduced development cycles, improved pipeline reliability, and measurable efficiency gains.