Principal Data & Analytics Engineer

SynergenX Health Holdings LLC•Houston, TX

5d•Hybrid

About The Position

SynergenX Health is seeking an experienced Principal Data & Analytics Engineer to build and maintain reliable data solutions using Azure and Databricks. This senior, hands-on individual contributor role combines data engineering, analytics engineering, solution architecture, and business problem solving. The engineer owns work from discovery and requirements gathering through architecture, development, testing, deployment, documentation, monitoring, and production support, while serving as a senior technical resource for the Data & AI team. The role involves developing production data pipelines and analytical products, translating business processes into consistent data logic, investigating discrepancies, and assessing downstream effects on reporting, KPIs, financial calculations, workflows, and dependent systems. The engineer will build quality controls and monitoring into solutions, modernize legacy data systems, strengthen analytical datasets and semantic models, and help establish engineering practices. They will also mentor teammates, review designs and code, integrate enterprise source systems, and support data foundations for automation, AI/ML, and advanced analytics. This is a full-time, exempt position based in Houston, Texas, with a hybrid arrangement requiring 2–3 days per week onsite at the Northwest Houston Corporate Headquarters. The schedule is Monday through Friday, 8:00 AM to 5:00 PM. The role reports to the Senior Manager, Data & AI. Candidates must be authorized to work in the United States, able to reliably commute to the Houston office, and complete pre-employment screening. The company does not provide current or future employment visa sponsorship.

Requirements

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.
Must be legally authorized to work in the United States.
Must be able to reliably commute to the Houston office and meet the onsite requirements.
Must successfully complete all pre-employment screening requirements.

Nice To Haves

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.

Responsibilities

Own complex data solutions from discovery and requirements gathering through architecture, development, testing, deployment, documentation, monitoring, and production support.
Build production ETL/ELT pipelines, Databricks workflows, curated datasets, dimensional models, semantic models, and integrations using SQL, Python/PySpark, Azure Data Factory, and Databricks.
Partner with Operations, Finance, Pharmacy, Clinical Operations, Marketing, and other teams to understand workflows and develop consistent data definitions and solutions.
Investigate data discrepancies across source systems, pipelines, transformations, and reporting layers; identify root causes and implement lasting fixes.
Evaluate how changes to shared datasets and business logic affect Power BI reports, KPIs, financial calculations, operational workflows, and dependent systems.
Implement automated validation, reconciliation, anomaly detection, monitoring, and alerting to identify pipeline failures, data drift, and unexpected historical changes.
Modernize existing data solutions by refactoring legacy pipelines, eliminating duplicated logic, improving performance, and migrating appropriate workloads to standardized Azure and Databricks patterns.
Develop and optimize datasets and semantic models that support consistent metrics and trustworthy historical reporting.
Help establish practical standards for Git, CI/CD, testing, documentation, reusable development patterns, data lineage, and deployment.
Mentor Data Engineers, BI developers, and analysts; conduct code and design reviews; and help resolve challenging technical issues.
Evaluate databases, APIs, SaaS platforms, EHR and pharmacy systems, financial applications, and other sources to determine appropriate ingestion strategies.
Build dependable data foundations and integrations for automation, AI/ML, and advanced analytics.