Senior Consultant – Data Strategy & AI Enablement

Torq•Plano, TX

2d•$105,000 - $140,000

About The Position

Torq is seeking experienced data strategy and functional data consultants to help clients connect data, analytics, and AI investments to meaningful business value. In this role, you will work at the intersection of business strategy, data management, analytics, technology, and organizational change. You will help clients assess their current capabilities, prioritize high-value opportunities, define future-state data ecosystems, establish governance and operating models, and create executable roadmaps. You will also help clients become ready to adopt AI responsibly and effectively by addressing the underlying data, process, governance, architecture, talent, and adoption capabilities required to scale AI successfully. This role is ideal for someone who can bring structure to ambiguity, facilitate senior stakeholder conversations, translate business needs into data and technology requirements, and stay connected to implementation through delivery.

Requirements

Consultant first mentality: understanding the business problem, bringing structure to ambiguity, communicating clearly, and taking ownership for delivering a successful outcome.
Independently lead meaningful pieces of work, build credibility with client stakeholders, and remain actively involved through delivery.
Ideally 5+ years of experience in data strategy, analytics consulting, management consulting, data governance, business analysis, data product management, digital transformation, or a related client-facing field.
Experience leading workstreams or significant deliverables within complex business and technology initiatives.
Strong problem-solving skills and the ability to structure ambiguous challenges, identify root causes, and develop practical recommendations.
Strong executive communication and storytelling skills, including experience creating and presenting clear recommendations to senior stakeholders.
Experience planning and facilitating interviews, workshops, working sessions, and cross-functional decision-making processes.
Ability to translate between business objectives, functional needs, data requirements, and technical considerations.
Strong stakeholder-management skills and the ability to build credibility across business, technology, data, analytics, risk, and compliance functions.
Ability to create high-quality assessments, roadmaps, operating models, business cases, requirements, and executive presentations.
Experience leading junior team members, reviewing deliverables, and creating structure for collaborative work.
A four-year degree in Business, Information Systems, Data Science, Computer Science, Engineering, Economics, Finance, or a related field, or equivalent professional experience.
Understanding of modern data ecosystems, including source systems, integration, storage, modeling, governance, analytics, and consumption.
Familiarity with cloud data platforms and technologies such as Microsoft Fabric, Databricks, Snowflake, Microsoft Azure, AWS, or Google Cloud Platform.
Experience assessing organizational data maturity and defining pragmatic current-state, future-state, and roadmap recommendations.
Understanding of centralized, decentralized, federated, data mesh, data-product, and analytics operating-model concepts.
Experience with data governance, data quality, metadata, lineage, stewardship, ownership, privacy, access, and compliance.
Ability to define and prioritize reporting, analytics, automation, machine-learning, and generative-AI use cases.
Understanding of the data requirements and organizational capabilities needed to support machine learning and generative AI.
Familiarity with AI-readiness considerations such as data accessibility, quality, governance, security, model risk, responsible AI, adoption, and value measurement.
Experience developing business requirements, functional requirements, user stories, process flows, and acceptance criteria.
Comfort using data to support analysis, build business cases, assess opportunities, and communicate recommendations.
Working knowledge of SQL, business intelligence, data modeling, or analytical tools is valuable, although this is not primarily an engineering role.
Must be currently authorized to work in the United States for any employer.

Nice To Haves

Enterprise data and analytics strategy
AI strategy and AI-readiness assessments
Generative-AI use-case identification and prioritization
Responsible AI and AI governance
Data-governance implementation
Data ownership and stewardship programs
Data-product strategy and product management
Data mesh and federated operating models
Analytics and business-intelligence operating models
Data literacy, adoption, and organizational change
Data-platform selection and implementation planning
Master data management
Metadata management, catalogs, and business glossaries
Microsoft Purview, Collibra, Informatica, or Unity Catalog
Power BI, Tableau, Looker, or similar analytics tools
Process improvement, value-stream mapping, and operating-model transformation
Financial modeling, benefits realization, and value tracking
Experience in automotive, airline, insurance, financial services, energy, healthcare, or consumer services

Responsibilities

Lead workstreams across data strategy, analytics strategy, AI readiness, data governance, operating-model design, and data-platform transformation initiatives.
Partner with business and technology leaders to understand strategic priorities, operational pain points, decision-making needs, and opportunities for data and AI.
Assess current-state data ecosystems across people, process, technology, governance, architecture, quality, and adoption.
Identify the business, data, and organizational barriers preventing clients from scaling analytics and AI use cases.
Facilitate executive interviews, stakeholder workshops, discovery sessions, working groups, and prioritization exercises.
Define data and AI visions, strategic principles, target-state capabilities, maturity models, and transformation roadmaps.
Identify, evaluate, and prioritize analytics and AI use cases based on business value, feasibility, data readiness, risk, and organizational capacity.
Develop business cases, investment recommendations, value frameworks, KPIs, and success measures for data and AI initiatives.
Design data operating models that clarify ownership, decision rights, governance bodies, delivery responsibilities, and interactions between business and technology teams.
Establish practical data-governance frameworks covering ownership, stewardship, quality, metadata, lineage, security, privacy, access, and lifecycle management.
Help clients define data domains, data products, critical data elements, business glossaries, and accountability models.
Translate business processes and stakeholder needs into data requirements, functional specifications, user stories, process maps, and acceptance criteria.
Partner with data engineers, architects, analysts, product teams, and AI specialists to ensure strategic recommendations are actionable and technically feasible.
Support vendor evaluations, platform selections, architecture decisions, and implementation planning.
Develop executive-level presentations, decision documents, assessments, playbooks, operating procedures, and implementation plans.
Support organizational adoption through stakeholder engagement, communications, training, change management, and capability-building activities.
Guide junior consultants by providing direction, reviewing deliverables, sharing context, and helping improve the quality of team output.
Contribute to Torq frameworks, assessments, accelerators, methodologies, and thought leadership across data, analytics, and AI.
Support business development by identifying client needs, shaping solution approaches, contributing to proposals, and participating in client presentations.