Need for Data Modeler/ Data Analyst on W2 @ Columbus, OH

Data Analyst/Data Modeler On W2 @ Columbus, OH

Job Title: Data Analyst / Data Modeler

Location: Columbus, OH 43215

Duration: 12+ Months

Complete skill matrix:

Data Analysis

Data Modeling - Hybrid, Dimensional and Relational

Data Visualization

Source to Target Mapping

Data Profiling

Hadoop plus

Responsibilities:

Critically evaluates information gathered from multiple sources, reconciles conflicts, classifies the information in logical categories. Uses different visualization techniques and present the results of data exploration exercises. Understands the flow of data, business processes/technical interfaces that would be created or impacted. Documents the source to target mappings for both data integration as well as web services (consumer/provider mappings) that can be easily understood by the project team members with data quality and transformation rules. Identifies and documents sources of existing data as well as the new data. Understands use of master and reference data including sources and contributors. Collaborates with data scientists and business partners and conducts data profiling and predictive analysis using a variety of standard tools. Creates conceptual, logical and physical data models and determines the most appropriate method to represent the data for business consumption. This includes all forms of physical representation of data, such as relational, dimensional, object, key-value (such as column families), and graph. Please note this list is not all inclusive. Have joint accountability with data stewards and data architects on the projects to ensure conformance to enterprise data governance policies around information risk and data protection guidelines. Data analysts should be familiar with common policies around data masking and protection schemes. Data source identification, data profiling, interpretation of patterns and trends, assessment of and improvement of data quality, versatility with visualization tools and techniques to share analysis findings. It is beneficial for individuals to have worked as a data analyst in any of the following types of projects: Analytically Intensive – Intended to draw/develop insights from data that is collected. Operationally Data Intensive – Intended to create authoritative data sources that are essential to core transactional processing. Data Integration Intensive e – Requires the merging of many data types to satisfy business requirements.