Lead Data Analyst at Ness Digital Engineering — NeverHard

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