Senior, Data Analyst

About the role

Position Summary... What you'll do... Data Source Identification Requires knowledge of Functional business domain and scenarios Categories of data and where it is held Business data requirements Database technologies and distributed datastores eg SQL NoSQL Data Quality Existing business systems and processes including the key drivers and measures of success To support the understanding of the priority order of requirements and service level agreements Help identify the most suitable source for data that is fit for purpose Perform initial data quality checks on extracted data Data Strategy Requires knowledge of understanding of business value and relevance of data and data enabled insights decisions Appropriate application and understanding of data ecosystem including Data Management Data Quality Standards and Data Governance Accessibility Storage and Scalability etc Understanding of the methods and applications that unlock the monetary value of data assets To understand articulate and apply principles of the defined strategy to routine business problems that involve a single function Understanding Business Context Requires knowledge of Industry and environmental factors Common business vernacular Business practices across two or more domains such as product finance marketing sales technology business systems and human resources and indepth knowledge of related practices Directly relevant business metrics and business areas To Provide recommendations to business stakeholders to solve complex business issues Develop business cases for projects with a projected return on investment or cost savings Translate business requirements into projects activities and tasks and aligns to overall business strategy and develops domain specific artifact Serve as an interpreter and conduit to connect business needs with tangible solutions and results Identify and recommend relevant business insights pertaining to their area of work Tech Problem Formulation Requires knowledge of Analyticsbig data analytics automation techniques and methods Business understanding Precedence and use cases Business requirements and insights To translate coown business problems within ones discipline to data related or mathematical solutions Identify appropriate methodstools to be leveraged to provide a solution for the problem Share use cases and gives examples to demonstrate how the method would solve the business problem Data Visualization Requires knowledge of Visualization guidelines and best practices for complex data types Multiple data visualization tools for example Python R libraries GGplot Matplotlib Ploty Tableau PowerBI etc Advanced visualization techniques tools Multiple story plots and structures OABCDE Communication influencing technique Emotional intelligence To generate appropriate graphical representations of data and model outcomes Understand customer requirements to design appropriate data representation for multiple data sets Work with User Experience designers and User Interface engineers as required to build front end applications Present to and influences the team and business audience using the appropriate data visualization frameworks and conveys clear messages through business and stakeholder understanding Customize communication style based on stakeholder under guidance and leverages rational arguments Guide and mentor junior associates on story types structures and techniques based on context Data Quality Management Requires knowledge of Data quality management techniques and standards Business metadata definitions and content data definitions Data profiling tools data cleansing tools data integration tools and issues and event management tools Understanding of users data consumption data needs and business implications Data modeling storage integration and warehousing Data quality framework and metrics User access best practices Enterprise data architecture modeling and design storage integration and warehousing Enterprise data quality framework and metrics Enterprise data strategy Enterprise data quality strategy Enterprise strategy to address regulatory and ethical requirements and policies around data privacy security storage retention and documentation To promote and educate others on data quality awareness Profile analyze and assess data quality Test and validate data quality requirements Continuously measure and monitor data quality Deliver against data quality service level agreements Manage operational Data Quality Management procedures Manage data quality issues and leads data cleansing activities to remove data quality defects improve data quality and eliminate unused data Determine user accessibility and removes or restricts user access as needed Interpret company and regulatory policies on data Educate others on data governance processes practices policies and guidelines Exploratory Data Analysis Requires knowledge of relevant Knowledge Discovery in Data KDD tools applications or scripting languages su

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Company Intel

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