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Lead Data Analyst

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Job Description - Lead Data Analyst


Description



Data Discovery & Analysis

  • Analyze all Netezza database tables to identify:
    • Business-critical and frequently used tables
    • Redundant and unused data assets
  • Perform data profiling and quality analysis to understand structure, patterns, and anomalies
  • Assess data dependencies and relationships across systems

Migration Assessment (Netezza → Azure)

  • Evaluate and report:
    • Number of tables already migrated to Azure
    • Tables pending migration
  • Validate data consistency between Netezza and Azure environments
  • Support migration planning through impact analysis

Data Mapping & Lineage

  • Create source-to-target data mapping documents
  • Establish end-to-end data lineage across systems
  • Document transformations and business rules applied during migration

Metadata & Documentation

  • Develop and maintain:
    • Metadata repository / definition documents
    • Data dictionary / taxonomy (asset library)
    • Table-level and column-level documentation
  • Ensure alignment with enterprise data governance standards

Data Modeling

  • Perform data modeling and mapping activities
  • Create:
    • Conceptual, logical, and physical data models
    • Domain-based models for retail or business-specific datasets
  • Design models optimized for:
    • Analytical workloads
    • Enterprise BI and reporting

Development & Implementation

  • Write and optimize SQL queries for data analysis and validation
  • Develop DDL scripts and assist in deploying data models across environments
  • Perform data wrangling and transformation using Python or similar tools
  • Work with data lakes and structured/unstructured data sources

Experience

  • 4+ years of experience in data analysis, data modeling, or data engineering roles

Technical Skills

  • Strong expertise in:
    • SQL (mandatory)
    • Python (or other scripting languages)
  • Experience with:
    • Netezza and cloud platforms (preferably Azure)
    • Data modeling tools (e.g., ERwin, ER/Studio, Azure tools)
    • Data warehousing and analytical workloads
  • Solid understanding of:
    • Data lineage and metadata management
    • ETL/ELT processes
    • Data migration strategies

Data Management Skills

  • Data profiling and analysis techniques
  • Data mapping and transformation documentation
  • Metadata, taxonomy, and data dictionary creation
  • Collaboration & Stakeholder Management

    • Work closely with:
      • Data engineers
      • BI/reporting teams
      • Data science teams
      • Business stakeholders
    • Gather and translate business requirements into technical data solutions
    • Support downstream data marts, semantic layers, and analytics use cases



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