Coordinator 2- Performance Management TI (Multiple Positions)

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

List most important duties first
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.

MAJOR DUTIES & RESPONSIBILITIES CONTINUED

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 t