Enterprise Data Analyst

Responsibilities

• Interpreting data, analyzing results using SQL, Excel, and basic statistical techniques
• Performing data analyses, analyzing data collection systems and other methods that optimize efficiency and data quality
• Acquiring data from primary or secondary data sources and maintaining databases
• Collecting, organizing, and interpreting database metadata (i.e., data structures, data relationships, data element descriptions), and capturing this in our Enterprise Data Catalog
• Modelling data, including master data, reference data, and transactional data objects, attributes, and relationships
• Assessing gaps and conflicts between data definitions across multiple software assets
• Creating database schemas from data models
• Writing SQL to analyze data and data structures in databases
• Assessing data quality in assigned data domains
• Providing guidance on master data and reference data governance
• Assisting with curating the Enterprise Data Catalog to scan new databases, data pipelines, data object definitions, Power
  
  BI reports, and related metadata

• Strong communication skills including interviewing subject matter experts (business and technical) to capture requirements, and effective writing skills to convey technical options and recommendations, and to produce required documentation such as functional design specifications
• At least 5 years of experience working with data modeling tools such as Visio, ERWin, or equivalent
• SQL Server Management Studio (SSMS) to analyze data content, data structures, and stored procedures
• Data warehouse design including normalized structures for Operational Data Stores, and star schema structures for data marts
• On premise and Azure cloud databases including SQL Server and Synapse

• Utility industry knowledge, especially gas utilities
• Knowledge of typical data patterns for shared services domains such as Finance, Human Resources, and IT
• Informatica data management tools (eg, Enterprise Data Catalog) including data profiling and data lineage analysis
• Knowledge of CMDB data models
• Data Governance principles and methods
• Reference data management, especially using Informatica Reference
  360