1

Afternoon Data Analyst R Programming Jobs in Tennessee

Responsible for data analysis, data management for the Engineering Decision Support department. Interfaces and coordinates with counterparts in multiple operating companies to obtain required input ...

Responsible for data analysis, data management for the Engineering Decision Support department. Interfaces and coordinates with counterparts in multiple operating companies to obtain required input ...

... other programming languages (i.e.,R, Python,). * Advanced pattern recognition and predictive ... analysis, time series modeling, etc. * A strong data skill set is also needed-- like SQL ...

... other programming languages (i.e.,R, Python,). * Advanced pattern recognition and predictive ... Implement analytics for smarter business processes. * Stay Updated:Keep up with industry ...

... programming and scripting languages such as, but not limited to, SQL, Python and R * Experience ... in data warehousing and analytics solutions * Experience deploying enterprise-wide reporting ...

next page

Showing results 1-20

Afternoon Data Analyst R Programming information

What is an Afternoon Data Analyst R Programming?

An Afternoon Data Analyst specializing in R Programming is a data professional who primarily works afternoon shifts and uses the R programming language to analyze, interpret, and visualize data. Their responsibilities typically include cleaning data, performing statistical analyses, and generating reports to support business decisions. They may work across various industries, collaborating with teams to provide insights and automate data processes using R. Afternoon shifts can be ideal for organizations that operate globally or require data support outside standard business hours. Proficiency in R, statistical techniques, and data visualization tools are essential skills for this role.

What are some common challenges faced by Afternoon Data Analysts working with R Programming, and how can they be addressed?

Afternoon Data Analysts using R Programming often encounter challenges such as handling large datasets efficiently, ensuring code reproducibility, and collaborating with team members across different shifts. To address these, it's helpful to utilize R packages designed for big data (like data.table or dplyr), maintain clear and well-documented scripts, and use version control systems like Git for seamless collaboration. Regular communication with team members during shift handovers and leveraging collaborative tools can also enhance workflow and reduce misunderstandings.

What is the difference between Afternoon Data Analyst R Programming vs Morning Data Analyst R Programming?

AspectAfternoon Data Analyst R ProgrammingMorning Data Analyst R Programming
Required CredentialsBachelor's in Data Science, Statistics, or related field; R programming skillsBachelor's in Data Science, Statistics, or related field; R programming skills
Work EnvironmentTypically in office settings, working during afternoon hoursOffice environment, working during morning hours
Employer & Industry UsageUsed in industries with shift-based operations like finance, healthcareCommon in similar industries, often with flexible scheduling
Search & Comparison IntentPeople comparing different shift roles or schedules in data analysisSimilar search intent focusing on shift timing differences

The main difference between Afternoon Data Analyst R Programming and Morning Data Analyst R Programming lies in their work hours. Both roles require similar skills, credentials, and are used in comparable industries. The choice depends on personal schedule preferences and employer shift structures.

What are the key skills and qualifications needed to thrive as an Afternoon Data Analyst specializing in R Programming, and why are they important?

To thrive as an Afternoon Data Analyst specializing in R Programming, you need a strong background in statistics, data analysis, and proficiency with R, often supported by a degree in a quantitative field. Experience with data visualization tools, R packages (like tidyverse), and familiarity with databases or version control systems (such as Git) is typically required. Critical thinking, attention to detail, and effective communication are essential soft skills for interpreting results and presenting insights to stakeholders. These skills ensure accurate data-driven decisions, efficient workflow, and the ability to translate complex data into actionable business strategies.
What are the most commonly searched types of Data Analyst R Programming jobs in Tennessee? The most popular types of Data Analyst R Programming jobs in Tennessee are:
What job categories do people searching Afternoon Data Analyst R Programming jobs in Tennessee look for? The top searched job categories for Afternoon Data Analyst R Programming jobs in Tennessee are:
What cities in Tennessee are hiring for Afternoon Data Analyst R Programming jobs? Cities in Tennessee with the most Afternoon Data Analyst R Programming job openings:
Infographic showing various Afternoon Data Analyst R Programming job openings in Tennessee as of June 2026, with employment types broken down into 41% Full Time, 35% Part Time, 12% Temporary, and 12% Contract. Highlights an 82% Physical, 7% Hybrid, and 11% Remote job distribution.
Senior Business Intelligence and Data Analyst

Senior Business Intelligence and Data Analyst

Smith & Wesson Brands, Inc.

Maryville, TN

Full-time

Posted 17 days ago


Job description

Senior Business Intelligence & Data Analyst

In-Office/Non-Remote in Maryville, TN

We are looking for a Modern BI / Data Warehouse Developer who can bridge traditional BI engineering (TSQL, ETL, SSIS-nice to have) with modern cloud analytics patterns, including building scalable data pipelines into Microsoft Fabric and creating transformations using notebooks (Python/Pandas) and related tooling.

This role focuses on data ingestion, modeling, transformations, and semantic layer readiness. While the role will not be responsible for building production reports, the ideal candidate understands reporting needs well enough to design data models and semantic models that support analytics and self-service BI.

COMPETENCIES AND SKILLS:

  • Strong hands-on experience with TSQL and relational data modeling.
  • Proven experience building ETL/ELT pipelines and supporting production data workflows.
  • Experience with Azure Data Factory (ADF) or comparable orchestration tools.
  • Experience building transformations using notebooks, including Python and Pandas (and/or Spark-based transformations as needed).
  • Strong understanding of:
  • modern data warehousing
  • dimensional modeling (facts/dimensions, SCDs, conformed dimensions)
  • performance fundamentals (indexes, partitioning concepts, query tuning as applicable)
  • Working knowledge of reporting concepts (requirements, visual performance considerations, data shaping), even if not building reports.
  • Nice to have skills:
  • TensorFlow, PyTorch, Hugging Face
  • SSIS experience (nice-to-have, not required).
  • Experience with Microsoft Fabric components (Lakehouse, Warehouse, pipelines, notebooks, shortcuts, etc.).
  • Familiarity with semantic modeling platforms and patterns (e.g., Power BI semantic models/tabular concepts).
  • Exposure to data governance, cataloging, and lineage practices.
  • Experience with CI/CD for data assets (Git integration, environment promotion).

ESSENTIAL DUTIES AND RESPONSIBILITIES:

  • Design and implement data ingestion pipelines to move data from source systems (SQL, files, APIs, SaaS apps) into Microsoft Fabric (e.g., Lakehouse/Warehouse).
  • Create and maintain pipelines using Azure Data Factory (ADF) and/or Fabric-native orchestration patterns as appropriate.
  • Build transformation logic using notebooks and modern approaches (Python, Pandas, Spark where applicable).
  • Apply best practices for:
    • data quality checks & validations
    • reproducibility (parameterization, modular notebooks, version control)
    • performance optimization (partitioning, pushdown, caching strategies where relevant)
  • Design and maintain enterprise data warehouse models
  • Understand how to prepare data for semantic models and analytics consumption:
  • Collaborate with report developers/analysts by ensuring data models align with real BI usage patterns.
  • Work closely with stakeholders (analysts, app teams, data owners) to translate requirements into scalable pipelines and models.
  • Participate in code reviews, documentation, and operational handoffs.
  • Help establish standards for naming, versioning, environments, and deployment patterns.

QUALIFICATIONS:

  • 3+ years of professional experience in BI, data engineering, or data warehouse development in an enterprise environment.
  • 2+ years of hands-on experience with TSQL, including:
    • complex joins, window functions, CTEs
    • query optimization and performance tuning
    • building and maintaining transformation logic in SQL
  • 2+ years of experience designing and implementing ETL/ELT pipelines.
  • 1+ years of experience building data pipelines using Azure Data Factory (ADF) or a comparable orchestration tool.
  • 3+ years of experience with modern data warehousing principles, including:
    • layered architectures (raw, curated, consumption)
    • ELT patterns batch and incremental loading strategies
  • 3+ years of hands-on dimensional data modeling experience, including:
    • star and snowflake schemas
    • fact and dimension table design
    • surrogate keys and SCD (Type 1/2) patterns
  • 1+ years of experience developing data transformations using notebooks.
  • Working knowledge of reporting and analytics tools concepts (e.g., how analysts and business users consume data), even if not responsible for building reports directly.
  • Professional communications skills, both verbal and written. Good team player.
  • Manage personal workload and work under tight timeframes.
  • Must be able to work independently with minimal supervision.
  • Bachelor's degree in Computer Science, Engineering, or a related field preferred.

PHYSICAL DEMANDS:

  • Occasional: bending, kneeling, squatting, standing, walking, reaching, overhead reaching, and fine motor skills

WORK ENVIRONMENT:

  • Normal office environment and office lighting
  • Within the Smith & Wesson manufacturing facility employees may be exposed to manufacturing noise, airborne liquid chemicals, fine particulate dust, ambient temperatures, and industrial lighting
  • All employees are required to apply ergonomic correctness to all job tasks

Updated 5/22/26