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Data Dimensions information

What are Data Dimensions?

Data dimensions are attributes or perspectives by which data can be categorized, organized, and analyzed. In the context of data management and analytics, dimensions such as time, geography, product, or customer allow users to slice and dice data for deeper insights. For example, a sales dataset might have dimensions like region and sales period, enabling users to analyze performance by different locations or times. Understanding data dimensions is essential for building effective reports, dashboards, and business intelligence solutions. They help transform raw data into meaningful information for decision-making.

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

To thrive as a Data Analyst, you need strong analytical skills, proficiency in statistics, and a background in mathematics or computer science. Familiarity with data analysis tools like SQL, Excel, Python, R, and data visualization platforms such as Tableau is typically required. Excellent problem-solving, attention to detail, and effective communication skills set top performers apart in this role. These skills are essential for extracting meaningful insights from data, supporting business decisions, and clearly conveying findings to stakeholders.

What are some common challenges faced by professionals working in data dimensions roles, and how can they overcome them?

Professionals in data dimensions roles often encounter challenges related to ensuring data quality, consistency, and proper integration across multiple data sources. Managing large volumes of data and maintaining accurate dimensional hierarchies can be complex, especially in organizations with rapidly evolving datasets. To overcome these challenges, it's important to establish clear data governance practices, collaborate closely with data engineers and business analysts, and leverage robust data management tools. Continuous learning and staying updated on best practices in data modeling and warehousing also contribute to long-term success in this field.
What are popular job titles related to Data Dimensions jobs in Tennessee? For Data Dimensions jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Data Dimensions jobs in Tennessee look for? The top searched job categories for Data Dimensions jobs in Tennessee are:
Data Dimensions jobs near you
Infographic showing various Data Dimensions job openings in Tennessee as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Senior Business Intelligence and Data Engineer

Smith and Wesson

Maryville, TN • On-site

Full-time

Posted 27 days ago

Job description

Senior Business Intelligence & Data Engineer
In-Office/Non-Remote in Maryville, TN
We are looking for a Modern BI / Data Warehouse Developer who can bridge traditional BI engineering (T-SQL, 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 T-SQL 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 T-SQL, 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 6/25/2026

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