Data Analyst II

Position Overview

Position Summary:

What you’ll do:

Understanding Business Context requires knowledge of industry and environmental factors, common business vernacular, and business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources. Deep knowledge of related practices, direct business metrics, and business areas support the development of business cases and recommendations, drive delivery of project activity, and support process updates and changes.

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 data stores such as SQL and No

SQL data quality, existing business systems, and key drivers and measures of success. Understand the appropriate data set required to develop simple models by developing initial drafts and support the identification of the most suitable source for data. Maintains awareness of data quality.

Tech Problem Formulation:
Requires knowledge of analytics, big data analytics, automation techniques and methods, business understanding, precedence and use cases, business requirements, and insights. Identify possible options to address the business problems within one discipline through relevant analytical methodologies and demonstrate understanding of use cases and desired outcomes.

Data Visualization:
Requires knowledge of visualization guidelines and best practices for complex data types. Multiple data visualization tools such as Python, R, libraries like ggplot, Matplotlib, plotly, Tableau, Power

BI, etc. Advanced visualization techniques, multiple story plots, structures, & 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 UX designers and UI engineers as required to build front end applications. Present to and influence the team and business audience using the appropriate data visualization frameworks and convey clear messages through business and stakeholder understanding.

Customize communication style based on stakeholder under guidance and leverage 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, content data definitions, data profiling tools, data cleansing tools, data integration tools, and issue and event management tools. Understand 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.

Promote data quality awareness, profile, analyze and assess data quality, test and validate data quality requirements under supervision of others, execute operational data quality management procedures under supervision of others, conduct data cleansing activities to remove data quality defects, improve data quality and eliminate unused data under supervision of others, grant user access to data, learn company and regulatory policies on data, learn data governance processes, practices, policies and guidelines.

Exploratory Data Analysis:
Requires knowledge of relevant knowledge discovery in data (KDD) tools and applications or scripting languages such as SQL, Oracle, Apache Mahout, MS Excel, Python, statistical techniques like mean, mode, median, variance, standard deviation, correlation, sorting, grouping, research analysis standards and activities, documentation procedures such as drafting, editing, bibliography format,…