Senior Data Engineer

Soleo Health Inc•Chesterfield, MO

56d

About The Position

Soleo Health is seeking a Senior Data Engineer to support our business needs in Chesterfield, MO. Join us in Simplifying Complex Care! The Senior Data Engineer will design, build, and optimize scalable data pipelines, architectures, and systems that enable data-driven decision-making across the organization. This role partners closely with business and technology stakeholders, including data analysts across multiple business units, to ensure that high-quality, well-structured data is accessible and trusted for analysis. The engineer will also deliver Power BI solutions and curated datasets that translate complex data into actionable business insights while maintaining a secure, reliable, and high-performing data platform.

Requirements

Bachelor’s degree in computer science, Information Technology, Engineering, Data Science, or a related field; master’s degree preferred.
5+ years of progressive experience as a Data Engineer, Data Platform Engineer, or similar data-focused role.
Strong hands-on experience with Snowflake, including schema design, ingestion pipelines, performance tuning, and administrative management.
Proven experience designing and managing data architectures using Azure services (Data Factory, Synapse, Data Lake, DevOps).
Proficiency in SQL and scripting/programming languages such as Python and/or Scala.
Experience designing domain-driven data models and building business-unit-specific data marts for analytics and visualization.
Familiarity with data integration patterns across multiple enterprise systems (e.g., EMR, nursing, finance, CRM) and structured/unstructured data sources.
Working knowledge of Power BI, including semantic modeling, dataflows, and DAX optimization.
Experience with data governance, metadata management, and compliance in regulated environments (healthcare or life sciences preferred).
Strong understanding of ETL/ELT methodologies, CI/CD practices, and version control for data engineering workflows.

Nice To Haves

Familiarity with AI-driven analytics and large language models (LLMs) is a plus—interest and willingness to learn in this area is strongly encouraged.

Responsibilities

Data Platform Management: Design, build, and maintain scalable, efficient ETL/ELT data pipelines integrating data from multiple source systems (e.g., EMR, nursing, financial, CRM, and operational platforms).
Cloud and Architecture: Build and manage modern data architectures leveraging Azure Cloud services (Azure Data Factory, Data Lake, Synapse) and Snowflake. Oversee schema design, data ingestion, and performance optimization.
Snowflake Administration: Manage Snowflake environments including user roles, warehouses, performance tuning, resource monitoring, and administrative tooling.
Domain Data Modeling: Develop and maintain domain-driven data models that reflect business structures and relationships across key functional areas.
Data Marts and Consumption: Build and optimize business-unit-centric data marts that enable self-service analytics, data visualization, and decision support.
Integration and Interoperability: Design and implement integration patterns to unify data across structured and unstructured sources, APIs, and third-party systems.
AI and Advanced Analytics: Leverage large language models (LLMs) and other AI/ML techniques to analyze curated datasets, identify trends, and drive actionable insights.
Data Quality and Governance: Establish and maintain data validation, reconciliation, and governance processes to ensure accuracy, consistency, and compliance with security and regulatory requirements.
Automation and Monitoring: Automate data workflows and build proactive monitoring and alerting frameworks for data pipeline health and performance.
Visualization and Reporting: Develop and maintain Power BI dashboards and semantic models that translate data into clear, business-relevant insights.
DevOps Enablement: Implement Azure DevOps and CI/CD practices for version control, testing, and automated deployment of data solutions.
Documentation and Collaboration: Maintain clear technical documentation for pipelines, data models, and architecture. Collaborate cross-functionally with business and technology teams to align data initiatives with strategic objectives.