Director of Data Engineering, Governance and Science

#WeAreONEOK – Fortune 500 company. 100+ years in business. Leading midstream service provider. Safety first. Sustainable operations. Environmentally responsible. Employee focused.

JOB SUMMARY

Job Profile Summary

The Director of Data is a senior leadership position responsible for developing and executing the organization’s comprehensive vision and strategy for data governance, engineering, and science. This pivotal role ensures that data is leveraged as a strategic asset to drive business growth, operational efficiency, and innovation across the enterprise. The Director of Data will be fostering a culture of data-driven decision making and lead cross-functional teams.  They will partner with technical and business senior leadership, and ensure the delivery of high-quality, reliable, sustainable, and secure data products and services. It requires a deep understanding of all disciplines within data principles, data trends, and security/regulatory requirements, as well as excellent analytical and communication skills. This role is a member of the expanded Data & Integrations Leadership Team.

Essential Functions and Responsibilities

• AI Visionary:

  • Thought leader in AI, guiding organizations on how to responsibly and effectively drive the value of embedding AI into its data ecosystem and business strategy.

  • Build a Practical Roadmap with short-term wins and long-term horizon while ensuring the AI vision aligns with existing platforms, strategies, and cybersecurity.

  • Measure and communicate value by defining KPIs for AI, communicating the impact and creating a continuous feedback loop.
     

• Guide and Influence Data Governance Program Design and Implementation:

  • Develop and enforce data governance frameworks, policies, and standards.

  • Ensure compliance with data privacy regulations (e.g., GDPR, CCPA, HIPAA).

  • Establish data stewardship programs and master data management practices.

  • Drive data literacy and accountability across business units.

  • Establish AI governance & ethics.
     

• Guide and Influence Data Engineering Design and Implementation:

  • Oversee and evolve scalable, secure, and high-performance data infrastructure (cloud/on-prem).

  • Oversee the design and evolve technologies supporting data modeling, ETL/ELT, pipelines, data quality, data lake house and data warehouse platforms.

  • Ensure data quality, reliability, and accessibility across systems.

  • Evaluate and integrate emerging technologies to enhance data capabilities.
     

• Guide and Influence Data Science Program Design and Implementation:

  • Establish strategic direction and mentor a team of data scientists and analysts to develop predictive models and insights.

  • Foster innovation through experimentation, continuous learning, enable A/B testing, and statistical modeling to inform tactical and strategic advance analytic investments.

  • Collaborate with delivery teams, corporate, commercial, and engineering/operations business units to solve complex business problems.

  • Promote the responsible adoption of AI/ML models to drive business efficiencies and automation.

  • Enable the organization through data readiness, talent & skills while developing understanding and reasonableness to fit the culture.
     

• Organizational readiness and Talent Development:

  • Develop organizational capability to design data readiness programs (data quality, data literacy and stewardship)

  • Promote “right-fit” adoption of advanced analytics, balancing ambition with cultural readiness and practical value.

  • Do-develop the data talent strategy, ensuring skills development aligns with business maturity and culture.
     

• Employee Education and Communication:

  • Design enterprise-wide data training programs, ensuring objectives and materials align to data strategy and educate employees and leaders on data policies, practices, and guidelines.

  • Strengthen subject matter expertise and develop thought-leadership across the data team. on, data strategy, AI and governance matters, providing guidance and support to other technology teams and business partners.
     

• Vendor Management:

  • Manage relationships with external vendors, such as consultants and technical service providers.  Vendor selection, contract negotiation, and consultant evaluation, to ensure the organization has access to relevant and needed services.