Data Analyst Location: Toronto (Hybrid)

Data Discovery & Analy sisAnalyze all Netezza database tables to identi fy:Business-critical and frequently used tab lesRedundant and unused data ass etsPerform data profiling and quality analysis to understand structure, patterns, and anomal iesAssess data dependencies and relationships across syst emsMigration Assessment (Netezza → Azu re)Evaluate and repo rt:Number of tables already migrated to Az ureTables pending migrat ionValidate data consistency between Netezza and Azure environme ntsSupport migration planning through impact analy sisData Mapping & Line ageCreate source-to-target data mapping docume ntsEstablish end-to-end data lineage across syst emsDocument transformations and business rules applied during migrat ionMetadata & Documentat ionDevelop and mainta in:Metadata repository / definition docume ntsData dictionary / taxonomy (asset libra ry)Table-level and column-level documentat ionEnsure alignment with enterprise data governance standa rdsData Model ingPerform data modeling and mapping activit iesCrea te:Conceptual, logical, and physical data mod elsDomain-based models for retail or business-specific datas etsDesign models optimized f or:Analytical worklo adsEnterprise BI and report ingDevelopment & Implementat ionWrite and optimize SQL queries for data analysis and validat ionDevelop DDL scripts and assist in deploying data models across environme ntsPerform data wrangling and transformation using Python or similar to olsWork with data lakes and structured/unstructured data sour cesExperie nce4+ years of experience in data analysis, data modeling, or data engineering ro lesTechnical Ski llsStrong expertise in:SQL (mandato ry)Python (or other scripting languag es)Experience wi th:Netezza and cloud platforms (preferably Azu re)Data modeling tools (e.g., ERwin, ER/Studio, Azure too ls)Data warehousing and analytical worklo adsSolid understanding of:Data lineage and metadata managem entETL/ELT proces sesData migration strateg iesData Management Ski llsData profiling and analysis techniq uesData mapping and transformation documentat ionMetadata, taxonomy, and data dictionary creat ionCollaboration & Stakeholder Managem entWork closely wi th:Data engine ersBI/reporting te amsData science te amsBusiness stakehold ersGather and translate business requirements into technical data soluti onsSupport downstream data marts, semantic layers, and analytics use ca

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