IAR Lead Data Analyst

Job Description

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 diligently. Breaks down complex goals into concrete tasks and activities, and actively supports leaders in project planning.
• Works actively to improve own skills and knowledge through internal and external, formal and informal, structured and unstructured learning. Is a lifelong learner and embodies a growth mindset. Stays abreast of innovative developments in their area of work and plays an active role in deploying them at the university.
• Supports leadership in strategic planning and contributes to operational excellence across the department.
• Understands and abides by the relevant policies and methods to access, use, transform, store, and delete data in responsible, secure, and compliant ways.
• Identifies data security risks when dealing with concrete data sets and takes adequate mitigation actions.
• Collaborates effectively with other technical specialists (e.g., data engineers) in the construction of data products, systems, and applications.
• Performs other job-related duties as assigned.

KSAs

• Advanced SQL proficiency, with experience writing queries and subqueries, modifying data (INSERT, UPDATE, DELETE), creating views, and knowledge of different join types, filtering, sorting, aggregation, window functions, common table expressions (CTE), and performance tuning.
• Highly proficient and experienced in using tools like Tableau and Power BI to present data and information utilizing charts, graphs, and maps in ways that make it easy to understand trends, patterns, and outliers.
• Ability to interpret and design models that describe how data relate to one another, and to the properties of the real-world entities they represent. Highly experienced in conceptual and logical data modeling, dimensional models, star schema, snowflake schema, and advanced concepts like slowly changing dimensions.
• Experienced with descriptive statistics, causal analysis, and inference. Familiarized with forecasting methods (time series, inferential, and/or regression) and with classification algorithms.
• Comfortable with common project management methodologies and frameworks (e.g., Waterfall, Agile, SDLC).
• Proficient in the MS Office suite, including advanced Excel knowledge.
• Proficient in flowchart and diagramming tools like Miro, Visio, Lucidchart, and similar applications.
• Familiarized with the university's most relevant KPIs, the drivers that affect them, and plays an active role in their definition and tracking.
• Ability to apply sound judgment, systems-thinking, and analytical skills to assess risks, perform root-cause analyses, make recommendations, and drive cross-functional decisions that contribute to the achievement of the university's objectives.
• Ability to perform with very high levels of autonomy, reliability, self-direction, and with a bias for action. Manages conflicting and concurrent activities with minimal need for supervision.

Minimum Qualifications

• Bachelor's Degree in a related discipline
• 7 years of related experience in Data Analysis, Business Intelligence, Data Science, Statistics, Decision Intelligence, Research, Learning Science, or Behavioral/Cognitive Psychology.
• Equivalent relevant experience performing the essential functions of this job may substitute for education degree requirements. Generally, equivalent relevant experience is defined as 1 year of experience for 1 year of education and is the discretion of the hiring manager.