Principal Data & Analytics Engineer
- Compensation
- Not disclosed
- Location
- Houston, TX
- Work setup
- Hybrid
- Seniority
- Principal
- Employment
- Full time
- Experience
- 7+ years
- Category
- Data analytics
- Posted
Skills
Must have
- 7+ years of progressive experience in data engineering, analytics engineering, BI engineering, or a related discipline.
- Demonstrated success independently delivering complex data solutions from initial requirements through production.
- Advanced SQL skills, including developing, optimizing, and troubleshooting complex data transformations.
- Strong Python and/or PySpark development experience.
- Strong hands-on experience with Databricks and Apache Spark.
- Strong Microsoft Azure experience, particularly Azure Data Factory and Azure data services.
- Strong data modeling skills, including dimensional models, star schemas, and analytical data models.
- Experience building and supporting production ETL/ELT pipelines.
- Experience implementing automated data quality validation, monitoring, and production alerting.
- Working knowledge of Power BI architecture, semantic models, and DAX sufficient to evaluate downstream analytical impacts.
- Experience with Git, CI/CD, and modern software and data engineering practices.
- Demonstrated ability to troubleshoot complex data discrepancies across multiple systems.
- Ability to investigate source systems, understand business processes, and deliver solutions when requirements are initially ambiguous.
- Ability to work directly with nontechnical stakeholders and translate business needs into technical solutions.
- Strong written documentation and verbal communication skills.
Nice to have
- Healthcare, pharmacy, EHR, or other regulated-data experience.
- Experience working with HIPAA-regulated data.
- Microsoft Fabric experience.
- Experience with Databricks Lakehouse, Delta Lake, and Unity Catalog.
- API integration experience.
- Experience with Azure DevOps.
- Experience modernizing legacy data warehouses or pipelines.
- Experience integrating operational and transactional systems with analytics platforms.
- Databricks Data Engineer certification.
- Microsoft Fabric or Power BI certification.
- Experience supporting AI/ML or automation workloads.
Full description
Principal Data & Analytics Engineer
SynergenX HealthJob 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.