Enterprise Practice Lead; Data; Remote from

What you’ll do:

• Define and maintain data governance, architecture, and analytics standards for practitioners across OIT. These are aligned to EA data patterns and grounded in the actual needs of practitioners working in agency delivery contexts.
• Translate enterprise data strategy into practical, usable guidance: what data practitioners need to do differently in their day-to-day work, not just what the strategy document says.
• Identify reusable data patterns, shared datasets, and common integration approaches that reduce duplication and raise data quality across the statewide portfolio.
• Spot governance gaps, data quality failures, and architecture drift across pods and surface them to EA and relevant OIT leadership before they become systemic problems.
• Run a data guild: data quality calibration sessions, architecture reviews, cross-agency knowledge exchange, and a practitioner culture that takes data integrity seriously.
• Develop a career pathway that spans the full range of data practice, from governance and stewardship to architecture, analytics, and platforms. The pathway should create growth options that do not require moving into management.
• Build and deliver training programs that address core statewide capability gaps, including governance rigor, data analytics for non-specialist audiences, architecture decision-making in constrained environments, and privacy compliance that practitioners understand rather than just follow.
• Create and maintain shared resources that practitioners can use without starting from scratch, such as data quality frameworks, governance templates, integration pattern libraries, and analytics standards guides.
• Advise ITDs and product directors on what strong data practice looks like across the governance, architecture, and analytics dimensions, and how to give meaningful feedback to practitioners whose craft spans a wide technical range.
• Distinguish between a practitioner skill gap, a tooling constraint, and a policy or architecture problem that sits above the practitioner level. Advise ITDs and product directors accordingly.
• Surface patterns of weak data practice across pods that point to training gaps or governance infrastructure failures rather than individual performance.

• Ability to connect data practice to delivery outcomes. Data governance and quality are easy to de-prioritize under delivery pressure. Build the community and make the case through specific examples of how strong data practice directly enables better products and services for Coloradans.
• Ability to build data literacy across non-data roles. Product managers, service designers, and delivery managers need to understand and use data more effectively. Build that literacy without requiring everyone to become a data specialist.
• Ability to hold privacy and security as the floor, not a constraint. Openness and protection are both real obligations. Design standards that enable data use while making PII/PHI safeguards non-negotiable, practitioners should understand why those safeguards exist, not just that they are required.
• Ability to make governance enabling, not bureaucratic. Data standards get adopted when practitioners experience them as making work easier and more credible. The Enterprise Lead designs governance with that test in mind and removes what fails it.
• Ability to lead across maturity levels. Different agencies and pods have very different data maturity, tooling environments, and technical capacity. Build standards that work at the low end without being remedial at the high end.

• Knowledge of data governance frameworks: metadata management, data quality standards, lineage documentation, stewardship models, and how to design governance that practitioners experience as enabling rather than bureaucratic.
• Knowledge of data architecture: integration patterns, data platforms, open data design, and how architecture decisions at the agency level connect to OIT’s enterprise data strategy and EA standards.
• Knowledge of data analytics methods and tooling: how to build shared analytical capability and what standards look like for analytics work that needs to be reproducible, defensible, and accessible to non-technical audiences including program leadership.
• Knowledge of state government data policy: privacy requirements for PII and PHI, security classifications, IDXR, the Statewide Longitudinal Data System (SLDS), and applicable federal data standards for programs Colorado administers.
• Knowledge of open data standards and constituent data transparency: how public data publishing works, what it requires technically and legally, and how it connects to OIT’s obligations to serve Coloradans and build public trust.
• Knowledge of career pathway design for data practitioners: how to build a competency framework that spans governance, architecture, analytics, and platform roles without flattening the significant skill differences between them, drawing on GDS’s data role family, data engineer, data analyst, data…