IAR Lead Data Analyst

About the role

The IAR Lead Data Analyst is responsible for extracting, processing, analyzing, and reporting on data to produce rigorous, actionable insights and research. They establish and maintain strong relationships with peers and leaders across IAR, Data Engineering, Product Management, Finance, EdTech, and Faculty staff. They perform ad hoc analyses, build standard reports and data visualizations, and deliver information and insights through various methods and media that privilege data storytelling and compress time-to-action. Job Duties Drives the documentation of data, analytics, and research needs in projects of high complexity with a student and equity-centered lens, collaborating with peers, cross-functional partners, faculty staff, and leaders. Leads the translation of user stories into technical requirements. Sets and manages expectations about complex analytics tasks and activities through clear, timely, and effective communication with partners and stakeholders. Answers complex business questions requiring extensive knowledge of the university's data assets across several domains and departments. Identifies adequate data sources and data sets to evaluate hypotheses and produce forecasts. Collaborates with Data Engineering in the development of complex ETL/ELT processes and data pipelines. Identifies, investigates, and solves complex data issues, contributing to the accuracy, completeness, consistency, timeliness, and validity of the university's data. Collaborates with Data Engineering and other data & analytics partners to define standards and best practices that increase data quality across the university. Combines data analysis, visualization, and narrative structures to convey information in compelling ways that instigate deliberate action. Participates in the definition of data visualization standards and best practices and promotes their adoption across the university. Utilizes software, scripts, and algorithms to perform data-related tasks (e.g. importing, cleaning, transforming, analyzing displaying) without human intervention. Conveys information effectively to peers, partners, and senior leaders, using a variety of resources and formats (synchronous and asynchronous, verbal and written) such as e-mails, presentations, meetings, and workshops. Creates and organizes information about processes, projects, operations, data assets, and insights from analyses and research, making it accessible in ways that increase the university's knowledge and efficiency. Writes and interprets technical documentation (e.g., Entity-Relationship, Conceptual, Logical, and Physical data models). Contributes actively to the development of the university's data management platforms (e.g., data dictionaries, catalogs, etc.). Plays a prominent role in other team members' development through constructive feedback and sharing of technical and institutional knowledge. Drives tasks, activities, and medium-scale projects with high levels of autonomy, confidence, and collaboration with peers and partners. Tracks and reports own progress, dependencies, and challenges dil