About GCU
Sourced by ZipRecruiter
Full-time
Posted 20 days ago
Are you a detaildriven data expert who loves transforming information into insights and improving business processes? We are seeking a Data Analyst II to join our Faculty Services team! We are seeking an analytical thinker who thrives in a collaborative environment and is passionate about data integrity, system optimization, and process automation.
In this role, you will take the lead in developing reports, dashboards, and workflows that enhance data quality and operational efficiency across multiple departments. You'll partner with crossfunctional teams-including IT, finance, legal, business development, and academic support groups-to design solutions, streamline processes, and ensure the accuracy and reliability of mission-critical information. If you enjoy solving complex data challenges, improving systems, and shaping datadriven decision-making, we'd love to meet you.
MUST reside in the Phoenix, Arizona area.
Primary Responsibilities /Accountabilities/ Essential Functions:
Retrieves data from internal and external systems and ensures accuracy through the development of reports, data analysis, and resolution of issues prior to submission/transfer for processing.
Develops queries, reports, dashboards, and processes to continuously monitor data/metadata quality and integrity.
Audits and maintains data quality/business processes/process documentation within various corporate databases and applications.
Assesses the business requirements for Faculty Services, automates processes and workflows, and expands or modifies software to serve new purposes or improve workflow or fit-for-use.
Analyzes, interprets, and communicates data performance metrics and feedback collection.
Reviews and controls data to ensure ongoing accuracy and consistency within Faculty Services.
Designs and integrates business processes and data flows between CRM/xRM and other applications.
Assists in designing operational dashboards for management.
Implements and deploys various applications as identified by management to maximize value.
Works with stakeholders, faculty support groups, business development, legal, finance, IT, and marketing/management teams on multiple projects, defining business requirements, coordinating system builds/customizations, reporting and data clean up.
Provides expertise to other teams including data administration and source-based data analysis.
Works independently on regular duties under general supervision, referring issues beyond scope of authority to management.
Performs root case analysis on data and identifies approaches to resolve.
Performs complex data analysis and report creation.
Takes the lead in customizing reports and software user needs and refers software programming issues to xRM department.
Has a greater understanding and knowledge of departmental policies and processes, related department programs and University policies.
Other duties as assigned.
Equipment Used and Responsibility
Must be highly proficient at Microsoft Excel with ability to manipulate delimited files using T-SQL.
Analyst skills using SQL, MS Excel, MS Access.
Basic math aptitude and understanding and an understanding of statistics.
Well versed in creating meaningful reports and dashboards.
Good understanding of end-to-end business processes.
Highly detail oriented with strong analytical and planning abilities
Self-motivated and results oriented.
Good interpersonal skills with ability to work well in a team environment.
Familiarity with Customer Relationship Software and developer tools.
Experience/ Education
Previous experience and/or education as a Data Analyst Level I.
Bachelor's degree from a regionally accredited institution in a related field.
A minimum of 3 years progressively responsible work experience (1 year Level I plus 2 years' additional work experience) using CRM preferably in a MS Dynamics or other MS environment, working on increasing more difficult projects, reports, queries utilizing SQL.
Java and JavaScript experience is helpful but not required.
Must successfully pass a background check.
Sourced by ZipRecruiter
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Q: What skills or qualities help someone succeed as a Data Analyst?
A: To succeed as a Data Analyst, key technical skills include proficiency in programming languages such as Python or R, expertise in data visualization tools like Tableau or Power BI, and knowledge of statistical analysis and machine learning concepts. Additionally, strong soft skills like effective communication, problem-solving, and collaboration are crucial for presenting insights to stakeholders and working with cross-functional teams. By combining these technical and soft skills, Data Analysts can drive business decisions, identify areas for improvement, and contribute to the growth and success of their organization.
Q: What is the career path for a Data Analyst?
A: A Data Analyst's typical career progression involves starting as an Entry-Level Data Analyst, where they collect, analyze, and interpret data to inform business decisions. As they gain experience, they can move into Mid-Level roles such as Senior Data Analyst or Business Analyst, where they take on more complex projects and lead smaller teams. Ultimately, they can advance to Senior Leadership positions like Data Scientist, Data Manager, or even Director of Analytics, where they oversee large-scale data initiatives and drive strategic business growth.\n\nKey opportunities for skill development and professional growth in this role include learning programming languages like Python or R, mastering data visualization tools like Tableau or Power BI, and staying up-to-date with emerging trends in machine learning and artificial intelligence. Additionally, Data Analysts can develop soft skills like communication, project management, and leadership to excel in their roles.\n\nLong-term career prospects for Data Analysts are diverse, with potential directions including transitioning into related fields like Business Intelligence, Data Engineering, or even becoming a Product Manager, or pursuing advanced degrees in Data Science or related fields to further specialize in areas like machine learning or data engineering.
