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Senior Python Data Analysis Jobs in Kentucky (NOW HIRING)

BrightSpring

Senior Data Modeler

Louisville, KY · Remote

Our Company BrightSpring Health Services Overview We are seeking a highly skilled Senior Data ... or Python optimization to maximize query speed and dataset availability for analytics and ...

Atria Senior Living

Financial Analyst

Louisville, KY · On-site

Atria Senior Living's corporate Support Center has openings for individuals looking for a career ... data analysis. * Strong analytical mindset with attention to detail and a commitment to data ...

PharMerica

Data & Reporting Analyst

Louisville, KY

Performs data analysis on retention and renewal performance, group purchasing partner pipeline ... Communicates directly with Director, Client Strategy and SVP, Client Strategy and Partnerships in ...

BrightSpring

Data & Reporting Analyst

Louisville, KY

$55K/yr

Performs data analysis on retention and renewal performance, group purchasing partner pipeline ... Communicates directly with Director, Client Strategy and SVP, Client Strategy and Partnerships in ...

PharMerica

Data & Reporting Analyst

Louisville, KY · On-site

$50K/yr

Performs data analysis on retention and renewal performance, group purchasing partner pipeline ... Communicates directly with Director, Client Strategy and SVP, Client Strategy and Partnerships in ...

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Senior Python Data Analysis information

What are the key skills and qualifications needed to thrive as a Senior Python Data Analyst, and why are they important?

To thrive as a Senior Python Data Analyst, you need an in-depth understanding of data analysis, statistical modeling, and advanced Python programming, typically supported by a degree in a quantitative field. Proficiency with data analysis libraries (like pandas, NumPy, and SciPy), visualization tools (such as Matplotlib and Seaborn), and experience with SQL databases are essential, and certifications like Microsoft Certified: Data Analyst Associate can be beneficial. Strong problem-solving abilities, effective communication, and the capacity to distill complex data insights for stakeholders are critical soft skills. These competencies enable you to extract actionable insights from large datasets, drive data-informed decision-making, and collaborate effectively across teams.

What are some common challenges Senior Python Data Analysts face when working with large datasets, and how can they overcome them?

Senior Python Data Analysts often encounter difficulties such as slow processing speeds, memory limitations, and data quality issues when handling large datasets. To overcome these challenges, it's essential to leverage efficient libraries like pandas and Dask, utilize optimized data formats (such as Parquet), and implement batch processing or cloud-based solutions. Collaborating closely with data engineers and IT teams also helps ensure robust data pipelines and infrastructure. Regular code optimization and staying updated on best practices can further enhance performance when working at scale.

What is a Senior Python Data Analyst?

A Senior Python Data Analyst is an experienced professional who uses Python programming to collect, process, and analyze large sets of data. They are responsible for extracting meaningful insights from data to support business decisions, often using libraries like pandas, NumPy, and matplotlib. In addition to technical skills, they also apply statistical analysis and data visualization techniques, and frequently mentor junior analysts or collaborate with data scientists and engineers. Their role may also involve developing automated data pipelines and ensuring data quality across projects.

What is the difference between Senior Python Data Analysis vs Data Scientist?

AspectSenior Python Data AnalysisData Scientist
Required SkillsPython, SQL, data visualization, statistical analysisPython, R, machine learning, statistical modeling
Work EnvironmentData analysis teams, business unitsResearch, product development, analytics teams
Industry UsageBusiness intelligence, finance, marketingTech, healthcare, finance, research
CertificationsPython certifications, data analysis coursesData science certifications, machine learning courses

While both roles involve Python and data handling, Senior Python Data Analysts focus on interpreting data and creating reports for business decisions, whereas Data Scientists develop predictive models and advanced algorithms to extract deeper insights. The roles often overlap, but Data Scientists typically require broader skills in machine learning and statistical modeling.

What are the most commonly searched types of Python Data Analysis jobs in Kentucky? The most popular types of Python Data Analysis jobs in Kentucky are:
What are popular job titles related to Senior Python Data Analysis jobs in Kentucky? For Senior Python Data Analysis jobs in Kentucky, the most frequently searched job titles are:
What job categories do people searching Senior Python Data Analysis jobs in Kentucky look for? The top searched job categories for Senior Python Data Analysis jobs in Kentucky are:
Senior Python Data Analysis jobs near you
Senior Data Modeler

Senior Data Modeler

BrightSpring Health Services

Louisville, KY • On-site

Full-time

Posted 26 days ago

BrightSpring Health Services rating

4.5

Company rating: 4.5 out of 10

Based on 59 frontline employees who took The Breakroom Quiz

218th of 228 rated social care providers

Job description

Our Company
BrightSpring Health Services
Overview
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.
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.

Qualifications
  • 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.

About our Line of Business
BrightSpring Health Services provides complementary home- and community-based health solutions for complex populations in need of specialized and/or chronic care. Through the Company's service lines, including pharmacy, home health care, and rehabilitation, we provide comprehensive and more integrated care and clinical solutions in all 50 states to over 475,000 customers, clients and patients daily. BrightSpring has consistently demonstrated strong and industry-leading quality metrics across its services lines, while improving the health and quality of life for high-need individuals and reducing overall healthcare system costs. For more information, please visit www.brightspringhealth.com. Follow us on Facebook, LinkedIn, and X.

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