Principal Data & Analytics Engineer

Principal Data & Analytics Engineer  SynergenX Health Job Type: Full-Time | Exempt Schedule: Monday–Friday | 8:00 AM – 5:00 PM Work Location: Houston, TX / Hybrid (2-3 days a week)- Northwest Houston Corporate Headquarters Principal Data & Analytics Engineer Build the Data Foundation Behind a Growing Healthcare Leader At SynergenX Health, technology and data help us improve patient care, strengthen operations, and make better business decisions. As one of the nation’s leading hormone optimization and wellness organizations, we’re investing in modern Azure and Databricks solutions to support our continued growth. We’re looking for an experienced Principal Data & Analytics Engineer who enjoys solving complex problems, working directly with business stakeholders, and building reliable data solutions from discovery through production. This is a senior, hands-on individual contributor role that combines data engineering, analytics engineering, solution architecture, and business problem solving. You’ll personally investigate, design, build, test, deploy, and monitor solutions while serving as a senior technical resource for our Data & AI team. Reports to: Senior Manager, Data & AI Role Type: Senior Individual Contributor Location: Houston, TX Work Arrangement: Hybrid — 2–3 days per week onsite What You’ll Do Own complex data solutions from start to finish. Lead discovery, requirements gathering, architecture, development, testing, deployment, documentation, monitoring, and production support. Build production data pipelines and analytical products. Develop ETL/ELT pipelines, Databricks workflows, curated datasets, dimensional models, semantic models, and integrations using SQL, Python/PySpark, Azure Data Factory, and Databricks. Turn business processes into reliable data logic. Partner with Operations, Finance, Pharmacy, Clinical Operations, Marketing, and other teams to understand their workflows and create consistent data definitions and solutions. Investigate difficult data discrepancies. Trace unexpected metric changes across source systems, pipelines, transformations, and reporting layers. Identify root causes and implement lasting fixes. Understand the downstream impact of changes. Evaluate how updates to shared datasets and business logic affect Power BI reports, KPIs, financial calculations, operational workflows, and dependent systems. Build quality and monitoring into every solution. Implement automated validation, reconciliation, anomaly detection, monitoring, and alerting to identify pipeline failures, data drift, and unexpected historical changes. Modernize existing data solutions. Refactor legacy pipelines, eliminate duplicated logic, improve performance, and migrate appropriate workloads to standardized Azure and Databricks patterns. Strengthen the analytics layer. Develop and optimize datasets and semantic models that support consistent metrics and trustworthy historical reporting. Improve engineering…