Associate Director, Data Governance Enablement

About Us

Johnson Controls, a global leader in thermal management, mission-critical building systems, energy efficiency, and decarbonization, helps customers use energy more productively, reduce carbon emissions, and operate with the precision and resilience required in rapidly expanding industries such as data centers, healthcare, pharmaceuticals, advanced manufacturing, and higher education.

For more than 140 years, Johnson Controls has delivered performance where it really matters. Backed by advanced technology, lifecycle services and an industry-leading field organization, we elevate customer performance, turn goals into real-world results and help move society forward.

Position Summary

The Data Governance Enablement Leader owns the strategy for how Johnson Controls governs its data. This role carries the governance assessment and playbook forward from findings into an operating reality, setting both the standards and the operating model that define how data is owned, managed, and trusted across the enterprise.

This is an influence role, not a delivery role. The standards are set here; execution sits with the Data Platform, Engineering, and Architecture teams, and this role partners with them closely to make the model real without taking over their work. Success depends on adoption, which means the person in this seat spends as much time building buy-in as building frameworks.

That advocacy runs in two directions. Inside DA&AI, the role builds a governance culture where good data practice is a default rather than a checkpoint. Across the business, it recruits and energizes the data owners and functional leaders whose participation the model depends on, and drives the change management that our assessment identified as the missing ingredient.

The role also keeps us moving forward on capability, guiding governance tooling decisions and pushing where governance can be done better than it is today, including applying AI to master data management, stewardship, and data quality.

Key Responsibilities

Core Responsibilities

• Governance strategy. Carry the governance assessment and playbook forward into a sequenced roadmap, and own the strategic direction and accountability of the program.

• Operating model and standards. Use playbook to define how DA&AI governs data: business data owner and steward roles, domain forums, decision rights, and the standards covering ownership, quality, classification, metadata, and lifecycle.

• Execution partnership. Work with Data Platform, Engineering, and Architecture to translate standards into implementation, and hold to committed outcomes without owning delivery.

• Governance culture within DA&AI. Make governance a default behavior across the organization through training, reusable artifacts, and consistent internal advocacy.

• Business influence and change management. Serve as the visible champion for governance across the business. Recruit data owners, build buy-in with functional leaders, and drive the adoption the model depends on.

• Tooling and innovation. Guide governance tooling decisions, including Microsoft Purview, and push new capability such as applying AI to master data management, stewardship, and data quality.

• Measurement and reporting. Define what good governance looks like quantitatively and report progress to organizational and executive audiences.

• AI governance alignment. Partner with the AI Enablement Leader, security, legal, and compliance so AI extends the enterprise data governance model.

• People leadership. Build and develop the governance team as the program scales, and mentor stewards and data owners outside the reporting line.

Success Profile — What Does Success Look Like at 12 Months?

The governance playbook is in force rather than on a shelf, with named business data owners active across the first priority domains; DA&AI treats governance as part of how work gets done instead of a review step; platform and engineering teams are building to a defined standard; and functions that once stood up their own shadow data operations now come to governance because it is the more trusted path.

Top 3–5 KPIs for the Team

Drafted for discussion. These are intended to measure governance outcomes rather than program activity, and should be finalized against available instrumentation and audit scope before the role is posted.

• Domain governance coverage : percentage of priority data domains with a named business data owner, an active steward, and progress in governance artifacts in place.

• Critical data element health: percentage of critical data elements with defined ownership, classification, and measured quality rules, with quality scorecards trending up quarter over quarter.

• Standards adoption: percentage of Tier 1 data products and platform work assessed as conforming to published governance standards.

• Business engagement: attendance and decision throughput in domain governance forums, and number of functions actively participating rather than nominally assigned.

• Governance maturity movement: change against the baseline governance assessment, measured on a consistent maturity model.

Qualifications & Experience

Education

• Bachelor’s degree in information management, computer science, business, or a related field. Advanced degree or governance certification (CDMP, DGSP) welcome but not required.

Experience

• 10+ years of professional experience in data management, governance, or data risk, including 3+ years leading a governance function or program at enterprise scale.

• Demonstrated experience standing up a federated governance model in a decentralized organization, including recruiting and enabling business data owners outside the reporting line.

• Direct experience supporting or responding to audits of data controls, including control design, evidence management, and remediation ownership.

• Experience in a matrixed environment where governance outcomes depend on teams the role does not own.

• People management experience.

Technical Skills

• Working knowledge of enterprise governance and catalog platforms, with hands-on familiarity with Microsoft Purview strongly preferred and exposure to comparable tools (Collibra, Alation) welcome.

• Understanding of modern data platform architecture sufficient to set credible standards: cloud warehouses including Snowflake, lakehouse and Fabric patterns, metadata and lineage, and the practical limits of catalog coverage in a multi-cloud estate.

• Literacy in relevant regulatory and control frameworks, including SOX ITGCs, privacy regulation such as GDPR, and data classification schemes. Familiarity with Data / AI governance frameworks (DAMA, NIST AI RMF, ISO/IEC 42001, EU AI Act) is a plus.

• Fluency in data quality measurement: rule definition, scorecarding, and tying quality metrics to business consequence.

Leadership Competencies

• Influence without authority, applied to senior business stakeholders who do not report to this role and may not want the accountability being assigned to them.

• Willingness to hold a standard and say no to exceptions that would erode it, including when the request comes from a senior leader.

• Genuine change leadership capability. The assessment named absent change management as a root cause, and this role is the correction.

• Composure and credibility under audit scrutiny.

• Judgment about scope. This role will be pulled toward doing the domains’ work for them and needs to resist it.

• Clear written and verbal communication for audiences ranging from data engineers to the executive committee and external auditors.

Additional Information

Work Location & Arrangement: Glendale, WI (Hybrid)

Travel: Up to 10% domestic travel may be required.

Sponsorship: Johnson Controls will not sponsor applicants for work visas or provide immigration-related employment sponsorship for this position, now or in the future.

#J-18808-Ljbffr