Enterprise Practice Lead (Data) (Remote from anywhere in CO)

Enterprise Practice Lead, Data

The Colorado Governor's Office of Information Technology is in the middle of a transformation worth joining. We're building cross-functional pod teams that partner directly with agencies to design, deliver, and continuously improve services that work for Coloradans. Good government depends on technology that works. And OIT exists to make that true for every Coloradan. The State of Colorado is seeking an Enterprise Practice Lead, Data to help build the next generation of public services. We're looking for people who want to do the best work of their careers in service of the public. The Enterprise Practice Lead, Data owns how data practice is executed across OIT, the standards, governance frameworks, architecture patterns, and professional community that make every data practitioner more capable and consistent. Data practice at OIT spans governance, architecture, analytics, and platforms, and the people doing that work are often distributed across different parts of the organization with limited peer connection. The Enterprise Lead builds the community and infrastructure that makes distributed data practice coherent and high-quality.

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 IT Directors 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 IT Directors 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.

What you bring:

• 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 governance manager, as reference points.

Minimum Qualifications, Substitutions, Conditions of Employment & Appeal Rights

A wide salary range is posted for this position and any job offer is based upon a salary analysis to comply with the Colorado Equal Pay for Equal Work Act. The salary analysis considers relevant experience, education, certifications, and state seniority as compared to others doing substantially similar work. While all offers are compliant with the Colorado Equal Pay for Equal Work Act, there is no guarantee an offer will be at the top of the posted range based on the salary analysis. This is a skills-based job announcement. The required minimum qualifications and/or education (if substituting for the proven experience, knowledge, and skills), are as follows:

• A minimum of five (5) years of experience in data governance and standards, which includes working in or leading cross-functional teams using iterative, user-centered and data-driven approaches
• A track record of working across disciplines — design, engineering, policy, or operations — to ship things that work for real people

Substitutions: Additional appropriate education will substitute for the required experience on a year-for-year basis, but cannot completely substitute for these qualifications. Training or Certification related to the work assigned to the position will be assigned credit towards substitution for experience and/or education, but cannot completely substitute for these qualifications. If the minimum qualifications include a degree requirement, additional appropriate paid or unpaid experience will substitute for the required education on a year-for-year basis.

Preferred Qualifications:

• Experience working with modern data governance frameworks, such as those related to the U.S. Digital Response (USDR) approaches to open data and language access.
• Experience shifting organizational culture from a "compliance-first" or bureaucratic governance model to one that emphasizes "enabling infrastructure" and practical craft standards that improve delivery outcomes.
• Direct experience managing or advising on complex public sector data initiatives, such as Statewide Longitudinal Data Systems (SLDS), multi-agency data sharing (IDXR), or navigating federal privacy standards for PII and PHI.

Conditions of Employment:

OIT employees must comply with any screening procedures in place at state agency locations where they might perform work. A pre-employment background check will be conducted as part of the selection process. Post-employment background checks will be required for specific agencies as business needs dictate, which may include a polygraph exam, fingerprint-based criminal history search, reference checks, and a drug test.

This position may require travel within the specified geographic area, and to locations across the state as needed.