Senior Data Modeler

BrightSpring Health Services•Louisville, KY

About The Position

We are seeking a highly skilled Senior Data Modeler to join our Data Engineering & Architecture team. This role will play a critical part not only in designing, developing, and maintaining logical and physical data models, but also in architecting, building, and optimizing the data pipelines and platforms that power our enterprise data warehouse, analytics ecosystem, and business intelligence solutions. This position ensures that data assets are structured, engineered, and delivered in a scalable, high performance, and user-friendly manner across the organization.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Management, or related field (or equivalent experience).
7–10 years of experience in data modeling, data engineering, dimensional modeling, or data architecture roles.
Strong knowledge of relational, dimensional, and NoSQL data modeling techniques.
Advanced SQL skills and experience designing for cloud data platforms (Databricks, Synapse, Azure SQL Databases, Redshift, BigQuery, or similar).
Expertise in building scalable ETL/ELT processes using modern data engineering tools (Azure Data Factory, Databricks, Synapse Pipelines, SSIS, etc.).
Strong proficiency with Python, PySpark, or Scala for data engineering and scripting.
Hands-on experience with Azure cloud data services: Azure Data Factory, Azure SQL Database, Azure Synapse Analytics, Azure Data Lake Storage Gen2, Databricks.
Experience designing and optimizing data lakes, delta lakehouse architectures, and large-scale distributed data systems.
Experience working with DevOps concepts—CI/CD pipelines, Git branching strategies, automated testing, and deployment.
Ability to orchestrate and influence remote teams, ensuring successful implementation of complex data solutions.
Detail-oriented with excellent organizational skills.
Effective working in a cross-functional, dynamic, and remote environment.
Strategic thinker with the ability to balance short-term deliverables with long-term platform evolution.

Nice To Haves

Hands-on experience designing, building, and operationalizing unified data platforms, including semantic layers, ontologies, and knowledge graphs, to enable AI/ML product development.
Experience with enterprise-scale analytics environments and BI tools (Power BI, Qlik, Tableau, Databricks AI/BI Dashboards).
Exposure to data governance, data cataloging, and MDM practices.
Knowledge of data vault modeling, star schema, and snowflake modeling.
Experience designing real-time/streaming data pipelines (Kafka, Event Hubs, Spark Streaming, etc.).
Familiarity with API platforms and tools such as Postman or API gateways.
Experience tuning large-scale Spark workloads and optimizing cloud compute costs.
Strong communication and collaboration skills across both technical and non-technical teams.

Responsibilities

Design, implement, and optimize conceptual, logical, and physical data models to support enterprise reporting, analytics, and data science use cases.
Collaborate with data engineers, business analysts, and business stakeholders to translate business requirements into robust data structures.
Define and enforce data modeling standards, best practices, and naming conventions across the organization.
Develop and maintain data dictionaries, ER diagrams, and metadata documentation to ensure clarity and consistency.
Analyze existing data models and workflows to identify opportunities for improvement in performance, scalability, and maintainability.
Contribute to the development of enterprise data architecture patterns and reusable modeling frameworks.
Architect, build, and optimize scalable ETL/ELT pipelines using modern data engineering frameworks and cloud technologies.
Lead the design and development of distributed data processing workflows using Databricks, PySpark, Azure SQL and/or Azure Synapse.
Develop and optimize data ingestion frameworks (batch and streaming) from diverse sources including FHIR, APIs, files, databases, and event streams.
Ensure data pipelines meet enterprise standards for performance, reliability, observability, and recoverability.
Perform advanced SQL, PySpark, or Python optimization to maximize query speed and dataset availability for analytics and downstream applications.
Oversee data lake and data warehouse architecture, including partitioning strategies, delta lake management, schema evolution, and performance tuning.
Troubleshoot, diagnose, and resolve complex data engineering and pipeline issues across cloud environments.
Mentor junior engineers and modelers, influencing engineering patterns, coding standards, and architectural direction.
Collaborate with security teams to implement proper access controls, encryption, secrets management, and compliance processes.