Data Analyst Jobs

About The Position

Requirements

• Degree in computer science, statistics, mathematics, data science, economics , or another related discipline is required. An equivalent combination of education and experience may be considered.
• 3+ years’ experience in work related to data management, data analytics, statistical analyses and predictive modelling.
• Proven ability to write SQL queries and to use SQL or other tools to extract, transform and load data for analysis.
• Proven ability to use R, Python, or equivalent for data analysis and visualization.
• Proven ability to use Python or another scripting language to automate tasks.
• Knowledge of statistical packages, ETL methods, and visual analytics.
• Demonstrated ability to streamline and automate processes.
• Critical thinking skills and a natural curiosity to understand processes and data relationships.
• Strong interpersonal, visual, and technical written communications skills.
• Effective collaborator, able to work with a variety of technical and non-technical roles across teams.
• Strong stakeholder management and prioritization skills.

Nice To Haves

• Experience with Microsoft Fabric or Power BI would be an asset.
• Knowledge of postsecondary education data domains or student data systems would be an asset.

Responsibilities

• Ensure Faculty data is accurate, consistent, properly maintained and shared in compliance with relevant privacy protection, confidentiality and other ethical principles.
• Identify and address data integrity/reliability issues and use data cleaning processes to achieve required data quality standards and identify data anomalies and discrepancies to appropriate institutional data stewards.
• Understand the available data, systems, dictionaries, and distribution channels from multiple information systems across the University and involving multiple domains.
• Execute and develop new Python scripts that enable data-informed operational processes for Faculty service units.
• Design and implement dimensional models across multiple subject areas for analytics solutioning.
• Create and maintain documentation as needed.
• Independently work with varied clients to identify, clarify, and fully understand their questions or problems, determine appropriate analytical approaches, and develop effective data solutions.
• Develop and apply deep understanding of University and Faculty policies, procedures, and guidelines (including but not limited to undergraduate studies regulations) to solution design and analysis.
• Analyze data using advanced statistical techniques (regression, clustering, decision trees etc.) to gain insights.
• Independently develop and apply reliable forecasting, predictive, and “what-if” models to address business questions and inform and support planning, evaluation, and budgeting processes.
• Assess and determine the appropriateness of machine learning algorithms, and other techniques to address business problems and optimize data solutions.
• Develop reports and relevant data visualizations (including dashboards, graphs and presentations and web applications) to inform evidence-based decision-making in the Faculty.
• Maintain Faculty-wide business intelligence reporting, ensuring consistent quality control practices and most recent data available.
• Develop queries using efficient SQL, applying expert understanding of databases and sound judgment of business requirements.
• Provide advice and recommendations based on data analysis, modelling, and interpretation of risks and opportunities.
• Communication findings, recommendations, and data through reports, presentations, and visual analytics to support evidence-based decision-making.
• Research new tools, systems, and practices to recommend improvements across all domains of the role.
• Understand the needs of each stakeholder group in order to assess and scope projects fully and to make recommendations on timelines and priorities for each.
• Communicate effectively with multiple, varied clients (senior leadership, faculty members, and staff) to understand needs, scope projects and project terms, update progress, and present findings.
• Ensure data and information is placed into the proper context by combining university data with both qualitative and quantitative environmental/external data and appropriate narrative.