· Build a practical understanding of current processes, stakeholders, dependencies, tools, and pain points across selected Enterprise Data workstreams.
· Identify where work is delayed by unclear ownership, manual handoffs, inconsistent standards, limited capacity, or low process adoption.
· Establish a focused improvement backlog and recommend the two to four opportunities most likely to produce measurable benefit during the engagement.
· Define baselines and success measures for the selected improvements.
· Improve selected processes such as demand management, prioritization, platform delivery, architecture decisions, governance reviews, support, status reporting, decision tracking, or dependency management.
· Design lightweight workflows, standards, roles, decision rights, service expectations, escalation paths, and operating rhythms.
· Make compliance easier to follow by reducing unnecessary steps, clarifying expectations, and embedding requirements into existing work.
· Create practical playbooks, templates, RACI models, decision logs, action registers, and process documentation.
· Work with process owners and managers to ensure that improvements are adopted rather than simply documented.
· Identify recurring manual work and control points that can be streamlined or automated.
· Design or coordinate appropriate workflows, forms, dashboards, notifications, evidence capture, and exception reporting using approved enterprise tools.
· Improve the quality and timeliness of management information on decisions, owners, risks, dependencies, overdue work, and milestones.
· Help teams capture information once and reuse it across delivery management, governance, reporting, and leadership reviews.
· Work with data engineering, platform, architecture, governance, PMO, business, and other partner teams.
· Facilitate focused working sessions to resolve ownership, priority, process, architecture, and dependency questions.
· Pilot improvements with selected teams, learn from actual usage, and refine the approach before broader rollout.
· Provide practical communication, training, and enablement so teams understand how to use the new processes and why they matter.
· Transition ownership, documentation, automation, and ongoing measurement to internal teams.
· Significant experience in data engineering, data platforms, data architecture, data products, or a closely related technical discipline.
· 8+ years of experience in enterprise data or technology environments, including experience leading cross-functional process improvement or operating-model change.
· Ability to understand technical delivery realities and translate them into workable processes, controls, decision rights, and operating rhythms.
· Experience improving adoption or compliance when teams are busy, distributed, or working across multiple tools and priorities.
· Experience with workflow automation, dashboards, reporting, metadata, governance, data quality, architecture, or platform enablement.
· Strong facilitation, written communication, executive communication, problem-solving, and stakeholder-management skills.
· Ability to influence without formal authority and build mechanisms that reduce dependence on individual follow-up.
· Ability to work independently and deliver tangible results within a nine-month engagement.
· Experience in retail, consumer, supply chain, merchandising, store operations, or another large-scale operational environment.
· Experience with hub-and-spoke, federated, data-product, or self-service analytics operating models.
· Experience with data governance, data quality, privacy, security, metadata, lineage, platform operations, or architecture review processes.
· Experience with Power BI, Power Automate, SharePoint, Teams, ServiceNow, Jira, Smartsheet, or comparable workflow and reporting tools.
· Experience with Lean, Six Sigma, Prosci, ADKAR, PMP, PgMP, or comparable improvement or change-management methods.
