AI-Native Data Engineer

AI-Native Data Engineer   Location: Tampa, FL / Remote  Department: Data & Analytics  Reports to: VP Data & Analytics About the Role The AI-Native Data Engineer designs, builds, and operates the data pipelines, data products, and platform capabilities that power enterprise analytics, operational intelligence, and AI-driven decision-making. The engineer uses AI coding assistants, intelligent automation, and agentic workflows to accelerate pipeline development, automate testing and documentation, detect and resolve data quality issues, and improve the reliability and efficiency of the enterprise data platform. Working within a modern Snowflake data ecosystem, the engineer integrates clinical, claims, revenue cycle, financial, HR, CRM, and operational data to create trusted, reusable, and AI-ready data products. Core Responsibilities Data Pipeline Development Use AI coding assistants and agentic development tools such as Claude Code, GitHub Copilot, Cursor, or equivalent to accelerate the design, development, testing, refactoring, and documentation of data pipelines. Build reusable, modular, and production-ready data engineering components using SQL, Python, dbt, and modern orchestration frameworks. Use AI to generate pipeline code, transformation logic, unit tests, integration tests, documentation, and deployment configurations, with appropriate engineering review and validation. Build, maintain, and optimize ELT/ETL pipelines using Snowflake, dbt, Airflow or Dagster, Fivetran, Matillion, and Python. Integrate data from eClinicalWorks (eCW), claims platforms, revenue cycle management systems, Oracle Fusion, HRIS, CRM, payer systems, and other enterprise applications Platform Engineering & Automation Develop CI/CD workflows for data pipelines using GitHub Actions, GitLab CI, Azure DevOps, or equivalent platforms Optimize Snowflake warehouses, query execution, storage, and compute consumption. Implement pipeline monitoring, alerting, dependency management, retry logic, and recovery mechanism Data Quality & Observability Implement automated data quality monitoring using tools like Monte Carlo, Bigeye, Soda, or Great Expectations Apply AI-assisted anomaly detection to identify unusual changes in clinical, claims, financial, and operational datasets Build automated data quality frameworks that detect missing records, duplicate data, schema changes, unexpected trends, reconciliation failures, and broken business rules Use AI agents to assist with incident triage, generate diagnostic queries, summarize failures, and propose remediation steps, with human approval for consequential changes Security, Compliance & Governance Implement HIPAAcompliant data handling, access controls, and audit logging Support data governance initiatives (data dictionary, lineage, stewardship) Ensure compliance with CMS, SOC2, and HITRUST requirements Qualifications   Required Skills 4–7 years of professional data engineering experience Demonstrated experience using AI coding assistants or AI-enabled development tools to improve engineering productivity Strong experience with Snowflake, including warehouses, tasks, streams, performance tuning, and cost optimization. Proficiency in dbt, including models, tests, macros, documentation, and deployment practices. Experience with orchestration tools such as Airflow or Dagster SQL (advanced) Python (data processing, APIs, automation) Git (branching, pull requests, code reviews) CI/CD (GitHub Actions, GitLab CI, Azure DevOps) Data quality frameworks (Great Expectations, Soda, Monte Carlo, Bigeye) Healthcare Data Skills Experience with EMR/RCM/claims data (Epic, Athena, eClinicalWorks, NextGen, Availity, Change Healthcare, etc.) Understanding of clinical, operational, and financial data domains Familiarity with VBC datasets (RAF, HCC, HEDIS, Stars, utilization) Cloud Skills Experience with AWS, Azure, or GCP Cloud storage (S3, ADLS, GCS) Serverless compute (Lambda, Cloud Functions optional)