Senior Data Modeler
Louisville, KY · Remote
Develop and maintain data dictionaries, ER diagrams, and metadata documentation to ensure clarity ... Ability to orchestrate and influence remote teams, ensuring successful implementation of complex ...
Senior Data Modeler
Louisville, KY · Remote
Develop and maintain data dictionaries, ER diagrams, and metadata documentation to ensure clarity ... Ability to orchestrate and influence remote teams, ensuring successful implementation of complex ...
Remote Metadata information
What are the key skills and qualifications needed to thrive in the Remote Metadata position, and why are they important?
To excel as a Remote Metadata Specialist, you need a deep understanding of information management, data organization, and metadata standards, often backed by a degree in library science, information systems, or a related field. Familiarity with metadata management tools (e.g., DAM systems, XML, Dublin Core), database systems, and sometimes certifications such as Certified Records Manager (CRM) are highly valued. Strong attention to detail, analytical thinking, and effective virtual communication are standout soft skills for this role. These competencies are essential to ensure data is accurately structured, easily retrievable, and supports organizational objectives in a distributed work environment.
What is a Remote Metadata job?
A Remote Metadata job involves managing, organizing, and ensuring the accuracy of metadata for digital content, databases, or other assets from a remote location. Responsibilities may include tagging, categorizing, and maintaining metadata standards to improve searchability and data integrity. These roles are common in industries like media, publishing, e-commerce, and information management. Strong attention to detail, familiarity with metadata standards, and proficiency in data management tools are often required.
What does a typical day look like for a Remote Metadata Specialist?
As a Remote Metadata Specialist, your day typically involves reviewing digital assets, assigning or updating metadata following industry standards, and collaborating with team members via virtual platforms. You may be responsible for quality-checking metadata for consistency and accuracy, troubleshooting data discrepancies, or developing new metadata taxonomies to improve searchability. Regular meetings with content creators, data managers, or IT professionals are common to align metadata practices with organizational goals. The role is often both independent and collaborative, offering a balance between focused tasks and teamwork, all within a flexible remote work environment.
What are popular job titles related to Remote Metadata jobs in Kentucky? For Remote Metadata jobs in Kentucky, the most frequently searched job titles are:
What job categories do people searching Remote Metadata jobs in Kentucky look for? The top searched job categories for Remote Metadata jobs in Kentucky are:
What cities in Kentucky are hiring for Remote Metadata jobs? Cities in Kentucky with the most Remote Metadata job openings:
Full-time
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BrightSpring Health Services rating
4.8
Based on 62 frontline employees who took The Breakroom Quiz
218th of 236 rated social care providers
Job description
Our Company
BrightSpring Health Services
OverviewWe 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.
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.
- 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.
Preferred
- 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.
Key Competencies
- Analytical and meticulous mindset with a strong ability to solve complex data design and engineering challenges.
- Ability to balance short-term deliverables with long-term enterprise strategy.
- Strong documentation and communication skills for presenting technical concepts to non-technical audiences.
- Leadership qualities with the ability to mentor and guide junior team members.
- Ability to think holistically across data modeling, data engineering, and data architecture disciplines.
What BrightSpring Health Services employees say
Pay
Benefits
Hours and flexibility
Workplace
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About BrightSpring Health Services
Sourced by ZipRecruiter
Industry
Health care and social assistance
Company size
10,000+ Employees
Headquarters location
Louisville, KY, US
Year founded
1974