Coordinator 2- Performance Management TI (Multiple Positions)

Location: Hattie Mae White Department: Performance Management Area: District Wide Contract Months: 12 Salary Range: $85,000.00 – $105,000.00 Academic Year: 26-27 JOB SUMMARY The Performance Management TIA Data Systems Coordinator 2 is responsible for the design, implementation, validation, and continuous improvement of all data systems supporting the district’s Teacher Incentive Allotment (TIA) program. This role ensures that student growth measures, teacher eligibility determinations, designation models, and funding projections are accurate, scalable, defensible, and compliant with TEA requirements. MAJOR DUTIES & RESPONSIBILITIES 1. Data Engineering & Integration (Foundational) Design and maintain longitudinal student–teacher–course datasets using PEIMS, assessment, and HR systems. Build automated pipelines to integrate and analyze: STAAR and EOC data; Local and vendor assessments (District Summative Assessments, NWEA MAP, DIBELS, CIRCLE, etc.); PEIMS course/service IDs; Teacher-of-record and co-teaching assignments. Ensure accurate student–teacher linkage at scale, including shared attribution and weighting. Conduct large-scale data quality audits to identify missing, invalid, or misattributed records. Produce internal documentation of all metrics, methodologies, and business rules. Support TEA submissions with accurate data exports and validation checks. 2. Student Growth Measure Development & Validation Develop and maintain student growth models across tested and non-tested subjects, including Student Growth Percentile models, Pre/post and gain-score models, Rubric- or portfolio-based growth models. Lead expansion of reliable growth measures for non-tested subjects to achieve ≥95% teacher eligibility. Conduct reliability, validity, and stability analyses to ensure fairness across campuses, subjects, and student groups. Maintain TEA-ready technical documentation for all growth methodologies. 3. Teacher Effectiveness & Designation Modeling Integrate student growth and observation data (e.g., Spot) into composite effectiveness scores. Model and simulate Recognized, Exemplary, and Master Teacher designation outcomes (will add Acknowledged Designation next year). Perform scenario analysis to test weighting schemes, thresholds, and policy changes. Support teacher-level reviews, appeals, and exception analyses with defensible data evidence. Provide regular data briefings to district leadership, principals, and TIA committees. Support development and maintenance of interactive dashboards (Power BI or similar) for teacher eligibility tracking, campus readiness and risk monitoring, and designation projections. 4. Funding & Allotment Forecasting – Cross-functional / Inter-departmental Build and maintain models projecting TIA allotment revenue under multiple designation and participation scenarios. Apply campus poverty and rurality multipliers to forecast district and campus-level funding. Support Finance and HR in modeling compensation distribution strategies aligned to district priorities. Support TEA. 5. Continuous Improvement & Strategic Analysis Identify participation gaps and growth-measure risk areas by subject, campus, or population. Analyze year-over-year trends to improve measure coverage and stability and correlation between student growth and teacher instruction. Respond to TEA policy updates and adjust models accordingly. 6. Other duties as assigned. EDUCATION Bachelor’s degree in Statistics, Data Science, Economics, Education Research, or related field. Master’s degree preferred. WORK EXPERIENCE 1 to 3 years. Preferred Experience: K–12 education data (assessment, PEIMS, accountability, teacher evaluations); Teacher evaluation or growth model development; Experience supporting high‑stakes accountability or funding systems; Familiarity with TEA accountability and TIA requirements; Knowledge of educational and human resource data systems (e.g., SIS, LMS, state reporting platforms, Oracle) is a plus; Knowledge of statistical modeling for creation of performance metrics. Excellent written and verbal communication skills; ability to explain complex data in a clear and actionable way. Soft Skills (Critical): Ability to explain complex models to non‑technical audiences; High attention to detail in high‑stakes decision‑making; Strong documentation and process discipline; Ability to work independently and manage multi‑month analytical projects. SKILL AND/OR REQUIRED LICENSING/CERTIFICATION Required Technical Skills Advanced proficiency in R and/or Python for statistical modeling and data automation. Advanced Excel (Power Query, complex formulas, large datasets). Strong understanding of statistical methods used in education research and evaluation (e.g., descriptive statistics, regression, t‑tests, ANOVA, chi‑square, confidence intervals, etc.). Experience working with large, messy, longitudinal datasets. Preferred Technical Skills Power BI proficiency (dashboard building, maintenance, data modeling, publishing). Power Apps proficiency (app creation, workflow automation, integration with data sources). Experience with student information systems (e.g., PowerSchool). Soft Skills (Critical) Ability to explain complex models to non‑technical audiences. High attention to detail in high‑stakes decision‑making. Strong documentation and process discipline. Ability to work independently and manage multi‑month analytical projects. LEADERSHIP RESPONSIBILITIES No supervisory responsibilities. May provide occasional work guidance, technical advice, and training to staff. WORKING/ENVIRONMENTAL CONDITIONS Work is normally performed in a typical interior work environment which does not subject the employee to any hazardous or unpleasant elements. Houston Independent School District is an equal opportunity employer. #J-18808-Ljbffr Houston Independent School District